From 12f538771816ca490c93351b1c8e85fb8bfd59a1 Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Tue, 10 Sep 2024 01:03:11 +0800 Subject: [PATCH 01/18] created testing1 and testing2 files --- testing - cs/testing1.py | 26 ++++++++++++++++++++++++++ testing - cs/testing2.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 54 insertions(+) create mode 100644 testing - cs/testing1.py create mode 100644 testing - cs/testing2.py diff --git a/testing - cs/testing1.py b/testing - cs/testing1.py new file mode 100644 index 0000000000..28efb71893 --- /dev/null +++ b/testing - cs/testing1.py @@ -0,0 +1,26 @@ +import numpy as np +import matplotlib.pyplot as plt +from qibolab.qubits import Qubit +from qibolab.instruments.emulator.readout import ReadoutSimulator + +bare_resonator_frequency = 5.045e9 + +qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-191.78e6) +readout = ReadoutSimulator(qubit=qb, g=30e6, noise_model=None, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) + +span = 1e6 +center_frequency = bare_resonator_frequency + 0.75e6 +freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) +y_gnd = np.abs(readout.ground_s21(freq_sweep)) +y_exc = np.abs(readout.excited_s21(freq_sweep)) + +freq_sweep /= 1e9 +plt.plot(freq_sweep, y_gnd, label=r"$|0\rangle$") +plt.plot(freq_sweep, y_exc, label=r"$|1\rangle$") +plt.ylabel("|S21| [arb. units]") +plt.xlabel("Readout Frequency [GHz]") +plt.legend() +plt.grid() +plt.tight_layout() +plt.savefig("S21.png", dpi=300) +plt.show() diff --git a/testing - cs/testing2.py b/testing - cs/testing2.py new file mode 100644 index 0000000000..e02e301301 --- /dev/null +++ b/testing - cs/testing2.py @@ -0,0 +1,28 @@ +import numpy as np +import matplotlib.pyplot as plt +from qibolab.qubits import Qubit +from qibolab.instruments.emulator.readout import ReadoutSimulator +from qibolab.pulses import ReadoutPulse + +bare_resonator_frequency = 5.045e9 +nshots = 100 + +SNR = 50 # dB +READOUT_AMPLITUDE = 1 +NOISE_AMP = np.power(10, -SNR / 20) +AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) + +qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-191.78e6) +readout = ReadoutSimulator(qubit=qb, g=10e6, noise_model=AWGN, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) +ro_pulse = ReadoutPulse(start=0, duration=1000, amplitude=READOUT_AMPLITUDE, frequency=5.04585e9, shape="Rectangular()", relative_phase=0) + +rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)] +rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)] +plt.scatter(np.real(rgnd), np.imag(rgnd), label=r"$|0\rangle$") +plt.scatter(np.real(rexc), np.imag(rexc), label=r"$|1\rangle$") +plt.xlabel("I") +plt.ylabel("Q") +plt.legend() +plt.tight_layout() +plt.savefig("IQ.png", dpi=300) +plt.show() \ No newline at end of file From 8ece2689a7f18aee34606296cc46d492164185be Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Tue, 10 Sep 2024 23:41:36 +0800 Subject: [PATCH 02/18] added readout_phase.py --- testing - cs/readout_phase.py | 47 +++++++++++++++++++++++++++++++++++ testing - cs/testing1.py | 4 ++- testing - cs/testing2.py | 2 +- 3 files changed, 51 insertions(+), 2 deletions(-) create mode 100644 testing - cs/readout_phase.py diff --git a/testing - cs/readout_phase.py b/testing - cs/readout_phase.py new file mode 100644 index 0000000000..68a0473f7b --- /dev/null +++ b/testing - cs/readout_phase.py @@ -0,0 +1,47 @@ +import numpy as np +import matplotlib.pyplot as plt +from qibolab.qubits import Qubit +from qibolab.instruments.emulator.readout import ReadoutSimulator +from qibolab.pulses import ReadoutPulse + +bare_resonator_frequency = 5.045e9 +nshots = 100 + +SNR = 50 # dB +READOUT_AMPLITUDE = 1 +NOISE_AMP = np.power(10, -SNR / 20) +AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) + +qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-191.78e6) +readout = ReadoutSimulator(qubit=qb, g=1000e6, noise_model=AWGN, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) +ro_pulse = ReadoutPulse(start=0, duration=1000, amplitude=READOUT_AMPLITUDE, frequency=5.04585e9, shape="Rectangular()", relative_phase=0) + +print(qb.bare_resonator_frequency-qb.drive_frequency) +span = 1000e6 +center_frequency = bare_resonator_frequency + 0.75e6 +freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) +y_gnd = np.abs(readout.ground_s21(freq_sweep)) +y_exc = np.abs(readout.excited_s21(freq_sweep)) + +freq_sweep /= 1e9 +plt.plot(freq_sweep, y_gnd, label=r"$|0\rangle$") +plt.plot(freq_sweep, y_exc, label=r"$|1\rangle$") +plt.ylabel(r"$\theta$ phase [rad]") +plt.xlabel("Readout Frequency [GHz]") +plt.legend() +plt.grid() +plt.tight_layout() +# plt.savefig("S21.png", dpi=300) +plt.show() + + +# rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)] +# rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)] +# plt.scatter(np.real(rgnd), np.imag(rgnd), label=r"$|0\rangle$") +# plt.scatter(np.real(rexc), np.imag(rexc), label=r"$|1\rangle$") +# plt.xlabel("I") +# plt.ylabel("Q") +# plt.legend() +# plt.tight_layout() +# #plt.savefig("IQ.png", dpi=300) +# plt.show() \ No newline at end of file diff --git a/testing - cs/testing1.py b/testing - cs/testing1.py index 28efb71893..f307ff8a01 100644 --- a/testing - cs/testing1.py +++ b/testing - cs/testing1.py @@ -22,5 +22,7 @@ plt.legend() plt.grid() plt.tight_layout() -plt.savefig("S21.png", dpi=300) +# plt.savefig("S21.png", dpi=300) plt.show() + + diff --git a/testing - cs/testing2.py b/testing - cs/testing2.py index e02e301301..3e5c02ca52 100644 --- a/testing - cs/testing2.py +++ b/testing - cs/testing2.py @@ -24,5 +24,5 @@ plt.ylabel("Q") plt.legend() plt.tight_layout() -plt.savefig("IQ.png", dpi=300) +#plt.savefig("IQ.png", dpi=300) plt.show() \ No newline at end of file From cc71386c067db206f5aaf223401c5a80a2fe5742 Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Thu, 12 Sep 2024 00:35:37 +0800 Subject: [PATCH 03/18] updated readout_phase.py to readout_example.py, pending changes to readout.py --- src/qibolab/instruments/emulator/readout.py | 51 +++++++++----- testing - cs/IQ.png | Bin 0 -> 126317 bytes testing - cs/S21.png | Bin 0 -> 113801 bytes testing - cs/phase.png | Bin 0 -> 119958 bytes testing - cs/readout_example.py | 70 ++++++++++++++++++++ testing - cs/readout_phase.py | 47 ------------- 6 files changed, 103 insertions(+), 65 deletions(-) create mode 100644 testing - cs/IQ.png create mode 100644 testing - cs/S21.png create mode 100644 testing - cs/phase.png create mode 100644 testing - cs/readout_example.py delete mode 100644 testing - cs/readout_phase.py diff --git a/src/qibolab/instruments/emulator/readout.py b/src/qibolab/instruments/emulator/readout.py index 4172b00114..be6d3d353c 100644 --- a/src/qibolab/instruments/emulator/readout.py +++ b/src/qibolab/instruments/emulator/readout.py @@ -6,23 +6,37 @@ from qibolab.qubits import Qubit -def lamb_shift(g, delta): - """Calculates the lamb shift of the readout resonator from its bare - frequency. +# def lamb_shift(g, delta): +# """Calculates the lamb shift of the readout resonator from its bare +# frequency. - Args: - g (float): Coupling strength between readout resonator and qubit in Hz. - delta (float): Detuning between readout resonator and qubit in Hz. +# Args: +# g (float): Coupling strength between readout resonator and qubit in Hz. +# delta (float): Detuning between readout resonator and qubit in Hz. - Returns: - Lambda (float): Lamb shift in Hz - """ - return g * g / delta +# Returns: +# Lambda (float): Lamb shift in Hz +# """ +# return g * g / delta + +# def dispersive_shift(g, delta, alpha): +# """Calculates the dispersive shift of the readout resonator for the first excited state of the qubit +# @see https://arxiv.org/abs/2106.06173, equation 35 + +# Args: +# g (float): Coupling strength between readout resonator and qubit in Hz. +# delta (float): Detuning between readout resonator and qubit in Hz. +# alpha (float): Qubit anharmonicity in Hz. + +# Returns: +# chi (float): Dispersive shift in Hz. +# """ +# return 2* alpha * g * g /delta / delta def dispersive_shift(g, delta, alpha): - """Calculates the dispersive shift of the readout resonator for the first excited state of the qubit - @see https://arxiv.org/abs/2106.06173, equation 35 + """Calculates the dispersive shift of the readout resonator for depending on state of the qubit + @see https://arxiv.org/pdf/1904.06560, equation 146 Args: g (float): Coupling strength between readout resonator and qubit in Hz. @@ -32,7 +46,7 @@ def dispersive_shift(g, delta, alpha): Returns: chi (float): Dispersive shift in Hz. """ - return 2 * alpha * g * g / delta / delta + return -1 *g *g /delta *(1/(1+(delta/alpha))) def s21_function(resonator_frequency, total_Q, coupling_Q): @@ -66,11 +80,12 @@ def __init__( sampling_rate (float): Sampling rate of the ADC/digitizer. """ - delta = qubit.bare_resonator_frequency - qubit.drive_frequency - ground_state_frequency = qubit.bare_resonator_frequency + lamb_shift(g, delta) - excited_state_frequency = ground_state_frequency + dispersive_shift( - g, delta, qubit.anharmonicity - ) + delta = np.abs(qubit.bare_resonator_frequency - qubit.drive_frequency) + ground_state_frequency = qubit.bare_resonator_frequency - dispersive_shift(g, delta, qubit.anharmonicity) + excited_state_frequency = qubit.bare_resonator_frequency + dispersive_shift(g, delta, qubit.anharmonicity) + # excited_state_frequency = ground_state_frequency + dispersive_shift( + # g, delta, qubit.anharmonicity + # ) self.noise_model = noise_model self.sampling_rate = sampling_rate diff --git a/testing - cs/IQ.png b/testing - cs/IQ.png new file mode 100644 index 0000000000000000000000000000000000000000..47c0751edd8dbc9ef184053323603c86cd1c0a83 GIT binary patch literal 126317 zcmeFZXINC()-}2@wh2U3K%$^vB#1~3wg?7-nH&V9P?F?KVcRMh0g(&}A|lBG$)QjQ 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cs/readout_example.py new file mode 100644 index 0000000000..79104e87c0 --- /dev/null +++ b/testing - cs/readout_example.py @@ -0,0 +1,70 @@ +import numpy as np +import matplotlib.pyplot as plt +from qibolab.qubits import Qubit +from qibolab.instruments.emulator.readout import ReadoutSimulator +from qibolab.pulses import ReadoutPulse + +bare_resonator_frequency = 5.045e9 +nshots = 100 + +SNR = 50 # dB +READOUT_AMPLITUDE = 1 +NOISE_AMP = np.power(10, -SNR / 20) +AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) + +qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-191.78e6) +readout = ReadoutSimulator(qubit=qb, g=15e6, noise_model=AWGN, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) +ro_pulse = ReadoutPulse(start=0, duration=1000, amplitude=READOUT_AMPLITUDE, frequency=5.0450000000001e9, shape="Rectangular()", relative_phase=0) + + +#demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator; +span = 1e6 +center_frequency = bare_resonator_frequency +freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) +print(readout.ground_s21(freq_sweep)) +y_gnd = np.abs(readout.ground_s21(freq_sweep)) +y_exc = np.abs(readout.excited_s21(freq_sweep)) + +# freq_sweep /= 1e9 +# plt.plot(freq_sweep, y_gnd, label=r"$|0\rangle$") +# plt.plot(freq_sweep, y_exc, label=r"$|1\rangle$") +# plt.ylabel("|S21| [arb. units]") +# plt.xlabel("Readout Frequency [GHz]") +# plt.legend() +# plt.grid() +# plt.tight_layout() +# # plt.savefig("S21.png", dpi=300) +# plt.show() + +# freq_sweep *= 1e9 + +# #demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator; (for domain of [-pi/2,pi/2]) +# y_gnd1 = np.angle(readout.ground_s21(freq_sweep)) +# y_exc1 = np.angle(readout.excited_s21(freq_sweep)) +# freq_sweep /= 1e9 +# plt.plot(freq_sweep, y_gnd1, label=r"$|0\rangle$") +# plt.plot(freq_sweep, y_exc1, label=r"$|1\rangle$") +# plt.ylabel(r"$\theta$ phase [rad]") +# plt.xlabel("Readout Frequency [GHz]") +# plt.legend() +# plt.grid() +# plt.tight_layout() +# # plt.savefig("phase.png", dpi=300) +# plt.show() + +#demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane +rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)] +rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)] +plt.scatter(np.real(rgnd), np.imag(rgnd), label=r"$|0\rangle$") +plt.scatter(np.real(rexc), np.imag(rexc), label=r"$|1\rangle$") +plt.xlabel("V_I") +plt.ylabel("V_Q") +plt.legend() +plt.tight_layout() +# plt.savefig("IQ.png", dpi=300) +plt.show() + + + + + diff --git a/testing - cs/readout_phase.py b/testing - cs/readout_phase.py deleted file mode 100644 index 68a0473f7b..0000000000 --- a/testing - cs/readout_phase.py +++ /dev/null @@ -1,47 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from qibolab.qubits import Qubit -from qibolab.instruments.emulator.readout import ReadoutSimulator -from qibolab.pulses import ReadoutPulse - -bare_resonator_frequency = 5.045e9 -nshots = 100 - -SNR = 50 # dB -READOUT_AMPLITUDE = 1 -NOISE_AMP = np.power(10, -SNR / 20) -AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) - -qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-191.78e6) -readout = ReadoutSimulator(qubit=qb, g=1000e6, noise_model=AWGN, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) -ro_pulse = ReadoutPulse(start=0, duration=1000, amplitude=READOUT_AMPLITUDE, frequency=5.04585e9, shape="Rectangular()", relative_phase=0) - -print(qb.bare_resonator_frequency-qb.drive_frequency) -span = 1000e6 -center_frequency = bare_resonator_frequency + 0.75e6 -freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) -y_gnd = np.abs(readout.ground_s21(freq_sweep)) -y_exc = np.abs(readout.excited_s21(freq_sweep)) - -freq_sweep /= 1e9 -plt.plot(freq_sweep, y_gnd, label=r"$|0\rangle$") -plt.plot(freq_sweep, y_exc, label=r"$|1\rangle$") -plt.ylabel(r"$\theta$ phase [rad]") -plt.xlabel("Readout Frequency [GHz]") -plt.legend() -plt.grid() -plt.tight_layout() -# plt.savefig("S21.png", dpi=300) -plt.show() - - -# rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)] -# rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)] -# plt.scatter(np.real(rgnd), np.imag(rgnd), label=r"$|0\rangle$") -# plt.scatter(np.real(rexc), np.imag(rexc), label=r"$|1\rangle$") -# plt.xlabel("I") -# plt.ylabel("Q") -# plt.legend() -# plt.tight_layout() -# #plt.savefig("IQ.png", dpi=300) -# plt.show() \ No newline at end of file From 5ac02614d8dbde1f0cc29821d0468377f4d7f9e9 Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Fri, 13 Sep 2024 17:53:05 +0800 Subject: [PATCH 04/18] modified demodulation procedure, incorporated dressed frequency, described relationship between bare resonator frequency with dispersive shifted (qubit-state dependent) resonator frequency, provided examples demonstrating usage of readout module --- src/qibolab/instruments/emulator/readout.py | 115 ++++++++------------ testing - cs/IQ.png | Bin 126317 -> 55399 bytes testing - cs/S21.png | Bin 113801 -> 113299 bytes testing - cs/phase.png | Bin 119958 -> 117941 bytes testing - cs/readout_example.py | 63 ++++++----- 5 files changed, 78 insertions(+), 100 deletions(-) diff --git a/src/qibolab/instruments/emulator/readout.py b/src/qibolab/instruments/emulator/readout.py index be6d3d353c..9e7fb536c5 100644 --- a/src/qibolab/instruments/emulator/readout.py +++ b/src/qibolab/instruments/emulator/readout.py @@ -6,37 +6,22 @@ from qibolab.qubits import Qubit -# def lamb_shift(g, delta): -# """Calculates the lamb shift of the readout resonator from its bare -# frequency. +def lamb_shift(g, delta): + """Calculates the lamb shift of the readout resonator from its bare + frequency. -# Args: -# g (float): Coupling strength between readout resonator and qubit in Hz. -# delta (float): Detuning between readout resonator and qubit in Hz. - -# Returns: -# Lambda (float): Lamb shift in Hz -# """ -# return g * g / delta - -# def dispersive_shift(g, delta, alpha): -# """Calculates the dispersive shift of the readout resonator for the first excited state of the qubit -# @see https://arxiv.org/abs/2106.06173, equation 35 - -# Args: -# g (float): Coupling strength between readout resonator and qubit in Hz. -# delta (float): Detuning between readout resonator and qubit in Hz. -# alpha (float): Qubit anharmonicity in Hz. - -# Returns: -# chi (float): Dispersive shift in Hz. -# """ -# return 2* alpha * g * g /delta / delta + Args: + g (float): Coupling strength between readout resonator and qubit in Hz. + delta (float): Detuning between readout resonator and qubit in Hz. + Returns: + Lambda (float): Lamb shift in Hz + """ + return g * g / delta def dispersive_shift(g, delta, alpha): """Calculates the dispersive shift of the readout resonator for depending on state of the qubit - @see https://arxiv.org/pdf/1904.06560, equation 146 + @see https://arxiv.org/pdf/1904.06560, equation 146, we omit the negative sign as we included it in our definition of delta Args: g (float): Coupling strength between readout resonator and qubit in Hz. @@ -46,7 +31,7 @@ def dispersive_shift(g, delta, alpha): Returns: chi (float): Dispersive shift in Hz. """ - return -1 *g *g /delta *(1/(1+(delta/alpha))) + return g *g /delta *(1/(1+(delta/alpha))) def s21_function(resonator_frequency, total_Q, coupling_Q): @@ -79,13 +64,14 @@ def __init__( coupling_Q (float): Coupling/external Q factor of the readout resonator. sampling_rate (float): Sampling rate of the ADC/digitizer. """ - - delta = np.abs(qubit.bare_resonator_frequency - qubit.drive_frequency) - ground_state_frequency = qubit.bare_resonator_frequency - dispersive_shift(g, delta, qubit.anharmonicity) - excited_state_frequency = qubit.bare_resonator_frequency + dispersive_shift(g, delta, qubit.anharmonicity) - # excited_state_frequency = ground_state_frequency + dispersive_shift( - # g, delta, qubit.anharmonicity - # ) + #maintaining the definition of |0> = |e> = (1 0) with the JC Hamiltonian model + delta = qubit.drive_frequency - qubit.bare_resonator_frequency + dressed_frequency = qubit.bare_resonator_frequency - lamb_shift(g,delta) + ground_state_frequency = dressed_frequency - dispersive_shift(g, delta, qubit.anharmonicity) + excited_state_frequency = dressed_frequency + dispersive_shift(g, delta, qubit.anharmonicity) + # print(f"ground_state_frequency= {ground_state_frequency}; excited_state_frequency= {excited_state_frequency}") + + self.lambshift = -lamb_shift(g,delta) self.noise_model = noise_model self.sampling_rate = sampling_rate @@ -100,8 +86,8 @@ def simulate_ground_state_iq(self, pulse: ReadoutPulse): Args: pulse (qibolab.pulses.ReadoutPulse): Qibolab readout pulse. """ - s21 = self.ground_s21(pulse.frequency) - return self.simulate_and_demodulate(s21, pulse) + s21 = self.ground_s21(pulse.frequency) + return self.simulate_and_demodulate(s21,pulse) def simulate_excited_state_iq(self, pulse: ReadoutPulse): """Simulates the IQ result for a given readout pulse when the qubit is @@ -110,12 +96,12 @@ def simulate_excited_state_iq(self, pulse: ReadoutPulse): Args: pulse (qibolab.pulses.ReadoutPulse): Qibolab readout pulse. """ - s21 = self.excited_s21(pulse.frequency) + s21 = self.excited_s21(pulse.frequency) return self.simulate_and_demodulate(s21, pulse) - def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse): + def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse): """Simulates the readout pulse for a given S21-parameter and - demodulates it. + homodyne demodulation/2nd stage of heterodyne demodulation. Args: s21 (complex): Complex S21 parameter. @@ -124,34 +110,23 @@ def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse): Returns: IQ (complex): IQ data for a shot. """ - logmag = np.abs(s21) - phase = np.angle(s21) - - env_I, env_Q = pulse.envelope_waveforms(self.sampling_rate / 1e9) - - start = int(pulse.start * 1e-9 * self.sampling_rate) - t = np.arange(start, start + len(env_I)) / self.sampling_rate - reference_signal_I = np.sin( - 2 * np.pi * pulse.frequency * t + pulse.relative_phase - ) - reference_signal_Q = np.cos( - 2 * np.pi * pulse.frequency * t + pulse.relative_phase - ) - - waveform = ( - logmag - * pulse.amplitude - * ( - env_I.data - * np.cos(pulse.relative_phase + phase) - * np.sin(2 * np.pi * pulse.frequency * t) - + env_Q.data - * np.sin(pulse.relative_phase + phase) - * np.cos(2 * np.pi * pulse.frequency * t) - + self.noise_model(t) - ) - ) - - return np.dot(waveform, reference_signal_I) + 1j * np.dot( - waveform, reference_signal_Q - ) + reflected_amplitude = np.abs(s21) + reflected_phase = np.angle(s21) + + env_I, env_Q = pulse.envelope_waveforms(self.sampling_rate / 1e9) #Gigasample per second + + start = int(pulse.start * 1e-9 * self.sampling_rate) #n = gigasample index + t = np.arange(start, start + len(env_I)) / self.sampling_rate #t_n + + #Low-pass filtered I-component (with intermediate frequency = carrier frequency) + I_filtered = reflected_amplitude*np.cos(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) + + #Low-pass filtered Q-component + Q_filtered = reflected_amplitude*np.sin(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) + + z = I_filtered+1j*Q_filtered + z *= np.exp(-1j*2*np.pi*t*pulse.frequency) + z = np.sum(z)/len(t) + + return z + \ No newline at end of file diff --git a/testing - cs/IQ.png b/testing - cs/IQ.png index 47c0751edd8dbc9ef184053323603c86cd1c0a83..02fc33d306dd8f44e4bc9df4bcab4309168f71ff 100644 GIT binary patch literal 55399 zcmeFacUY6zx;`9r9B0IaqM%X~R75F*h)B19jMR}T-2nxWCN;DW%Zwt`21r*FkS@Ih zhy{pB6DdIgL_ul@5rI$w>0U@+@X|E#8u!K}H0!K{Ax!}stP;vK6G@PG22>SsL-TocN@FlW@;nw{Pv63&-wY 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index 79104e87c0..c555f2433e 100644 --- a/testing - cs/readout_example.py +++ b/testing - cs/readout_example.py @@ -12,53 +12,56 @@ NOISE_AMP = np.power(10, -SNR / 20) AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) -qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-191.78e6) -readout = ReadoutSimulator(qubit=qb, g=15e6, noise_model=AWGN, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) -ro_pulse = ReadoutPulse(start=0, duration=1000, amplitude=READOUT_AMPLITUDE, frequency=5.0450000000001e9, shape="Rectangular()", relative_phase=0) +qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-263e6) +readout = ReadoutSimulator(qubit=qb, g=10e6, noise_model=AWGN, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) #demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator; span = 1e6 center_frequency = bare_resonator_frequency freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) -print(readout.ground_s21(freq_sweep)) y_gnd = np.abs(readout.ground_s21(freq_sweep)) y_exc = np.abs(readout.excited_s21(freq_sweep)) -# freq_sweep /= 1e9 -# plt.plot(freq_sweep, y_gnd, label=r"$|0\rangle$") -# plt.plot(freq_sweep, y_exc, label=r"$|1\rangle$") -# plt.ylabel("|S21| [arb. units]") -# plt.xlabel("Readout Frequency [GHz]") -# plt.legend() -# plt.grid() -# plt.tight_layout() -# # plt.savefig("S21.png", dpi=300) -# plt.show() +freq_sweep /= 1e9 +plt.plot(freq_sweep, y_gnd, label=r"$|0\rangle$") +plt.plot(freq_sweep, y_exc, label=r"$|1\rangle$") +plt.ylabel("|S21| [arb. units]") +plt.xlabel("Readout Frequency [GHz]") +plt.legend() +plt.grid() +plt.tight_layout() +# plt.savefig("S21.png", dpi=300) +plt.show() -# freq_sweep *= 1e9 +freq_sweep *= 1e9 -# #demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator; (for domain of [-pi/2,pi/2]) -# y_gnd1 = np.angle(readout.ground_s21(freq_sweep)) -# y_exc1 = np.angle(readout.excited_s21(freq_sweep)) -# freq_sweep /= 1e9 -# plt.plot(freq_sweep, y_gnd1, label=r"$|0\rangle$") -# plt.plot(freq_sweep, y_exc1, label=r"$|1\rangle$") -# plt.ylabel(r"$\theta$ phase [rad]") -# plt.xlabel("Readout Frequency [GHz]") -# plt.legend() -# plt.grid() -# plt.tight_layout() -# # plt.savefig("phase.png", dpi=300) -# plt.show() +#demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator; (for domain of [-pi/2,pi/2]) +y_gnd1 = np.angle(readout.ground_s21(freq_sweep)) +y_exc1 = np.angle(readout.excited_s21(freq_sweep)) +freq_sweep /= 1e9 +plt.plot(freq_sweep, y_gnd1, label=r"$|0\rangle$") +plt.plot(freq_sweep, y_exc1, label=r"$|1\rangle$") +plt.ylabel(r"$\theta$ phase [rad]") +plt.xlabel("Readout Frequency [GHz]") +plt.legend() +plt.grid() +plt.tight_layout() +# plt.savefig("phase.png", dpi=300) +plt.show() #demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane +ro_frequency = 5.0450e9 + readout.lambshift +ro_pulse = ReadoutPulse(start=0, duration=1000, amplitude=READOUT_AMPLITUDE, frequency=ro_frequency, shape="Rectangular()", relative_phase=0) + rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)] rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)] plt.scatter(np.real(rgnd), np.imag(rgnd), label=r"$|0\rangle$") plt.scatter(np.real(rexc), np.imag(rexc), label=r"$|1\rangle$") -plt.xlabel("V_I") -plt.ylabel("V_Q") +# plt.xlim([0,1]) +# plt.ylim([-1,1]) +plt.xlabel(r"$V_I$") +plt.ylabel(r"$V_Q$") plt.legend() plt.tight_layout() # plt.savefig("IQ.png", dpi=300) From b6d8a2ea08bff9534113838201cc3c41c1e4947e Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Fri, 13 Sep 2024 18:05:08 +0800 Subject: [PATCH 05/18] updated readout_example folder --- {testing - cs => readout_example}/IQ.png | Bin {testing - cs => readout_example}/S21.png | Bin {testing - cs => readout_example}/phase.png | Bin .../readout_example.py | 0 testing - cs/testing1.py | 28 ------------------ testing - cs/testing2.py | 28 ------------------ 6 files changed, 56 deletions(-) rename {testing - cs => readout_example}/IQ.png (100%) rename {testing - cs => readout_example}/S21.png (100%) rename {testing - cs => readout_example}/phase.png (100%) rename {testing - cs => readout_example}/readout_example.py (100%) delete mode 100644 testing - cs/testing1.py delete mode 100644 testing - cs/testing2.py diff --git a/testing - cs/IQ.png b/readout_example/IQ.png similarity index 100% rename from testing - cs/IQ.png rename to readout_example/IQ.png diff --git a/testing - cs/S21.png b/readout_example/S21.png similarity index 100% rename from testing - cs/S21.png rename to readout_example/S21.png diff --git a/testing - cs/phase.png b/readout_example/phase.png similarity index 100% rename from testing - cs/phase.png rename to readout_example/phase.png diff --git a/testing - cs/readout_example.py b/readout_example/readout_example.py similarity index 100% rename from testing - cs/readout_example.py rename to readout_example/readout_example.py diff --git a/testing - cs/testing1.py b/testing - cs/testing1.py deleted file mode 100644 index f307ff8a01..0000000000 --- a/testing - cs/testing1.py +++ /dev/null @@ -1,28 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from qibolab.qubits import Qubit -from qibolab.instruments.emulator.readout import ReadoutSimulator - -bare_resonator_frequency = 5.045e9 - -qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-191.78e6) -readout = ReadoutSimulator(qubit=qb, g=30e6, noise_model=None, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) - -span = 1e6 -center_frequency = bare_resonator_frequency + 0.75e6 -freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) -y_gnd = np.abs(readout.ground_s21(freq_sweep)) -y_exc = np.abs(readout.excited_s21(freq_sweep)) - -freq_sweep /= 1e9 -plt.plot(freq_sweep, y_gnd, label=r"$|0\rangle$") -plt.plot(freq_sweep, y_exc, label=r"$|1\rangle$") -plt.ylabel("|S21| [arb. units]") -plt.xlabel("Readout Frequency [GHz]") -plt.legend() -plt.grid() -plt.tight_layout() -# plt.savefig("S21.png", dpi=300) -plt.show() - - diff --git a/testing - cs/testing2.py b/testing - cs/testing2.py deleted file mode 100644 index 3e5c02ca52..0000000000 --- a/testing - cs/testing2.py +++ /dev/null @@ -1,28 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from qibolab.qubits import Qubit -from qibolab.instruments.emulator.readout import ReadoutSimulator -from qibolab.pulses import ReadoutPulse - -bare_resonator_frequency = 5.045e9 -nshots = 100 - -SNR = 50 # dB -READOUT_AMPLITUDE = 1 -NOISE_AMP = np.power(10, -SNR / 20) -AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) - -qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-191.78e6) -readout = ReadoutSimulator(qubit=qb, g=10e6, noise_model=AWGN, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) -ro_pulse = ReadoutPulse(start=0, duration=1000, amplitude=READOUT_AMPLITUDE, frequency=5.04585e9, shape="Rectangular()", relative_phase=0) - -rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)] -rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)] -plt.scatter(np.real(rgnd), np.imag(rgnd), label=r"$|0\rangle$") -plt.scatter(np.real(rexc), np.imag(rexc), label=r"$|1\rangle$") -plt.xlabel("I") -plt.ylabel("Q") -plt.legend() -plt.tight_layout() -#plt.savefig("IQ.png", dpi=300) -plt.show() \ No newline at end of file From 23e1b7962bbadd7dd300be4cc019ffaa5e6ad3ab Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 13 Sep 2024 10:26:22 +0000 Subject: [PATCH 06/18] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- readout_example/readout_example.py | 45 ++++++++++------ src/qibolab/instruments/emulator/readout.py | 60 ++++++++++++--------- 2 files changed, 65 insertions(+), 40 deletions(-) diff --git a/readout_example/readout_example.py b/readout_example/readout_example.py index c555f2433e..50e2db07f9 100644 --- a/readout_example/readout_example.py +++ b/readout_example/readout_example.py @@ -1,24 +1,37 @@ -import numpy as np import matplotlib.pyplot as plt -from qibolab.qubits import Qubit +import numpy as np + from qibolab.instruments.emulator.readout import ReadoutSimulator from qibolab.pulses import ReadoutPulse +from qibolab.qubits import Qubit bare_resonator_frequency = 5.045e9 nshots = 100 -SNR = 50 # dB +SNR = 50 # dB READOUT_AMPLITUDE = 1 NOISE_AMP = np.power(10, -SNR / 20) AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) -qb = Qubit(0, bare_resonator_frequency=bare_resonator_frequency, drive_frequency=3.99e9, anharmonicity=-263e6) -readout = ReadoutSimulator(qubit=qb, g=10e6, noise_model=AWGN, internal_Q=2.5e6, coupling_Q=6e4, sampling_rate=1966.08e6) +qb = Qubit( + 0, + bare_resonator_frequency=bare_resonator_frequency, + drive_frequency=3.99e9, + anharmonicity=-263e6, +) +readout = ReadoutSimulator( + qubit=qb, + g=10e6, + noise_model=AWGN, + internal_Q=2.5e6, + coupling_Q=6e4, + sampling_rate=1966.08e6, +) -#demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator; +# demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator; span = 1e6 -center_frequency = bare_resonator_frequency +center_frequency = bare_resonator_frequency freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) y_gnd = np.abs(readout.ground_s21(freq_sweep)) y_exc = np.abs(readout.excited_s21(freq_sweep)) @@ -36,7 +49,7 @@ freq_sweep *= 1e9 -#demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator; (for domain of [-pi/2,pi/2]) +# demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator; (for domain of [-pi/2,pi/2]) y_gnd1 = np.angle(readout.ground_s21(freq_sweep)) y_exc1 = np.angle(readout.excited_s21(freq_sweep)) freq_sweep /= 1e9 @@ -50,9 +63,16 @@ # plt.savefig("phase.png", dpi=300) plt.show() -#demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane +# demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane ro_frequency = 5.0450e9 + readout.lambshift -ro_pulse = ReadoutPulse(start=0, duration=1000, amplitude=READOUT_AMPLITUDE, frequency=ro_frequency, shape="Rectangular()", relative_phase=0) +ro_pulse = ReadoutPulse( + start=0, + duration=1000, + amplitude=READOUT_AMPLITUDE, + frequency=ro_frequency, + shape="Rectangular()", + relative_phase=0, +) rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)] rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)] @@ -66,8 +86,3 @@ plt.tight_layout() # plt.savefig("IQ.png", dpi=300) plt.show() - - - - - diff --git a/src/qibolab/instruments/emulator/readout.py b/src/qibolab/instruments/emulator/readout.py index 9e7fb536c5..2ba07d9dc7 100644 --- a/src/qibolab/instruments/emulator/readout.py +++ b/src/qibolab/instruments/emulator/readout.py @@ -19,6 +19,7 @@ def lamb_shift(g, delta): """ return g * g / delta + def dispersive_shift(g, delta, alpha): """Calculates the dispersive shift of the readout resonator for depending on state of the qubit @see https://arxiv.org/pdf/1904.06560, equation 146, we omit the negative sign as we included it in our definition of delta @@ -31,7 +32,7 @@ def dispersive_shift(g, delta, alpha): Returns: chi (float): Dispersive shift in Hz. """ - return g *g /delta *(1/(1+(delta/alpha))) + return g * g / delta * (1 / (1 + (delta / alpha))) def s21_function(resonator_frequency, total_Q, coupling_Q): @@ -64,14 +65,18 @@ def __init__( coupling_Q (float): Coupling/external Q factor of the readout resonator. sampling_rate (float): Sampling rate of the ADC/digitizer. """ - #maintaining the definition of |0> = |e> = (1 0) with the JC Hamiltonian model + # maintaining the definition of |0> = |e> = (1 0) with the JC Hamiltonian model delta = qubit.drive_frequency - qubit.bare_resonator_frequency - dressed_frequency = qubit.bare_resonator_frequency - lamb_shift(g,delta) - ground_state_frequency = dressed_frequency - dispersive_shift(g, delta, qubit.anharmonicity) - excited_state_frequency = dressed_frequency + dispersive_shift(g, delta, qubit.anharmonicity) + dressed_frequency = qubit.bare_resonator_frequency - lamb_shift(g, delta) + ground_state_frequency = dressed_frequency - dispersive_shift( + g, delta, qubit.anharmonicity + ) + excited_state_frequency = dressed_frequency + dispersive_shift( + g, delta, qubit.anharmonicity + ) # print(f"ground_state_frequency= {ground_state_frequency}; excited_state_frequency= {excited_state_frequency}") - self.lambshift = -lamb_shift(g,delta) + self.lambshift = -lamb_shift(g, delta) self.noise_model = noise_model self.sampling_rate = sampling_rate @@ -86,8 +91,8 @@ def simulate_ground_state_iq(self, pulse: ReadoutPulse): Args: pulse (qibolab.pulses.ReadoutPulse): Qibolab readout pulse. """ - s21 = self.ground_s21(pulse.frequency) - return self.simulate_and_demodulate(s21,pulse) + s21 = self.ground_s21(pulse.frequency) + return self.simulate_and_demodulate(s21, pulse) def simulate_excited_state_iq(self, pulse: ReadoutPulse): """Simulates the IQ result for a given readout pulse when the qubit is @@ -96,12 +101,12 @@ def simulate_excited_state_iq(self, pulse: ReadoutPulse): Args: pulse (qibolab.pulses.ReadoutPulse): Qibolab readout pulse. """ - s21 = self.excited_s21(pulse.frequency) + s21 = self.excited_s21(pulse.frequency) return self.simulate_and_demodulate(s21, pulse) - def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse): - """Simulates the readout pulse for a given S21-parameter and - homodyne demodulation/2nd stage of heterodyne demodulation. + def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse): + """Simulates the readout pulse for a given S21-parameter and homodyne + demodulation/2nd stage of heterodyne demodulation. Args: s21 (complex): Complex S21 parameter. @@ -112,21 +117,26 @@ def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse): """ reflected_amplitude = np.abs(s21) reflected_phase = np.angle(s21) - - env_I, env_Q = pulse.envelope_waveforms(self.sampling_rate / 1e9) #Gigasample per second - start = int(pulse.start * 1e-9 * self.sampling_rate) #n = gigasample index - t = np.arange(start, start + len(env_I)) / self.sampling_rate #t_n - - #Low-pass filtered I-component (with intermediate frequency = carrier frequency) - I_filtered = reflected_amplitude*np.cos(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) + env_I, env_Q = pulse.envelope_waveforms( + self.sampling_rate / 1e9 + ) # Gigasample per second + + start = int(pulse.start * 1e-9 * self.sampling_rate) # n = gigasample index + t = np.arange(start, start + len(env_I)) / self.sampling_rate # t_n + + # Low-pass filtered I-component (with intermediate frequency = carrier frequency) + I_filtered = reflected_amplitude * np.cos( + 2 * np.pi * t * pulse.frequency + pulse.relative_phase + reflected_phase + ) + self.noise_model(t) - #Low-pass filtered Q-component - Q_filtered = reflected_amplitude*np.sin(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) + # Low-pass filtered Q-component + Q_filtered = reflected_amplitude * np.sin( + 2 * np.pi * t * pulse.frequency + pulse.relative_phase + reflected_phase + ) + self.noise_model(t) - z = I_filtered+1j*Q_filtered - z *= np.exp(-1j*2*np.pi*t*pulse.frequency) - z = np.sum(z)/len(t) + z = I_filtered + 1j * Q_filtered + z *= np.exp(-1j * 2 * np.pi * t * pulse.frequency) + z = np.sum(z) / len(t) return z - \ No newline at end of file From 142fda72f95aba136d32c8a7271910b67bfa1c9c Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Sat, 14 Sep 2024 09:37:17 +0800 Subject: [PATCH 07/18] provided additional descriptions, improved performance of the readout setup (dispersive effect, SNR effect) --- 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N}76zx4pKSczx-0E*6CoSIXQ_lq_mMn2D2?NDdAO$UANj7N*S- zcFjIwaC&X7nXf$Z6AX;B`N-Onq+n~aotPU^>7l1jJoI*csPcSlLc`%*wv)AYi9D7a zbVcqIrooAdYZb6-HTQL1I@oR*!9wyifLh0_1}Yz7jH;72LEz7E!pdqPdxR$=Il234 z(+JLmE_Bc~zvsYWVTaC5Wo_K&^%o+dG=9hO;&==pniC>x6Zw+|**0S1v+5yz*s9z5 z0jBT$#c+o_8PdzQ*siw9>B@g{3ZfL)8{A(v#G+q}NSPGpczNL|c?nrJA2iNQ6QNNO zq4|n`r}UTP(1~Y zcV|uM6zoYTtiL)^jU1km8S^d&D8CvG3}QsAO-U?|;*A)&j!-IVBXN7D zBAty`dsW{)!X>t<)OU9L@s^2?jZ;KT_ia`h zM#fkR^QmXgX@7s=0**636Oui@?sj{kyN^eK4JXw8r%qB~d3&(|COd5@Gtxb=;xZPdpC(PoU5H+IO-;wg&CYqA!(0$Olt3$G;pX(F zaoj;l$i%DBL5B*(o|O0QRML1L@YiKa1GpqZDeb!txkaHp)69mKi$~QVk%QRnwOV^sL(~0 zIg}xcAH)VPdV~)=I#4t*?yB5(rJt%WPCJnU_dVl>+$&df&A|=ue7OG>g=l(ph&b|K ztsWWo(&;^3+Sp}H4eocfK%dwbhbzi#GFBnnJ?* = |e> = (1 0) with the JC Hamiltonian model + #ground_state_frequency = dressed resonator frequency when qubit is in ground state (vice versa for excited_state_frequency) delta = qubit.drive_frequency - qubit.bare_resonator_frequency - dressed_frequency = qubit.bare_resonator_frequency - lamb_shift(g,delta) - ground_state_frequency = dressed_frequency - dispersive_shift(g, delta, qubit.anharmonicity) - excited_state_frequency = dressed_frequency + dispersive_shift(g, delta, qubit.anharmonicity) - # print(f"ground_state_frequency= {ground_state_frequency}; excited_state_frequency= {excited_state_frequency}") - + ground_state_frequency = qubit.bare_resonator_frequency - lamb_shift(g,delta) - dispersive_shift(g, delta, qubit.anharmonicity) + excited_state_frequency = qubit.bare_resonator_frequency - lamb_shift(g,delta) + dispersive_shift(g, delta, qubit.anharmonicity) + self.lambshift = -lamb_shift(g,delta) self.noise_model = noise_model self.sampling_rate = sampling_rate @@ -119,12 +119,11 @@ def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse): t = np.arange(start, start + len(env_I)) / self.sampling_rate #t_n #Low-pass filtered I-component (with intermediate frequency = carrier frequency) - I_filtered = reflected_amplitude*np.cos(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) - + i_filtered = reflected_amplitude*np.cos(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) #Low-pass filtered Q-component - Q_filtered = reflected_amplitude*np.sin(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) + q_filtered = reflected_amplitude*np.sin(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) - z = I_filtered+1j*Q_filtered + z = i_filtered+1j*q_filtered z *= np.exp(-1j*2*np.pi*t*pulse.frequency) z = np.sum(z)/len(t) From 9216b68ef350e68161ead39908555ea5e6d2e556 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 14 Sep 2024 02:15:09 +0000 Subject: [PATCH 08/18] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- readout_example/readout_example.py | 40 ++++++++--------- src/qibolab/instruments/emulator/readout.py | 50 +++++++++++++-------- 2 files changed, 50 insertions(+), 40 deletions(-) diff --git a/readout_example/readout_example.py b/readout_example/readout_example.py index b6b1bc960b..48044db3a6 100644 --- a/readout_example/readout_example.py +++ b/readout_example/readout_example.py @@ -8,10 +8,10 @@ bare_resonator_frequency = 5.045e9 nshots = 100 -SNR = 40 # dB +SNR = 40 # dB READOUT_AMPLITUDE = 1 -NOISE_AMP = np.power(10, -SNR/20) -AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t))*4.5e1 +NOISE_AMP = np.power(10, -SNR / 20) +AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 4.5e1 qb = Qubit( 0, @@ -29,11 +29,11 @@ ) -#demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator; -#we first prepare a centre frequency for frequency sweeping, which may be modified during analysis, to search for the dispersive shift -#note that dispersive shift and lamb shift depends on detuning(i.e.: drive_frequency - bare_resonator_frequency) -#lamb shifted frequncy would then be shifted dispersively depending on ground_state or excited state of qubit -#fitting of |S21| is discussed here: https://github.com/qiboteam/qibocal/pull/917 +# demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator; +# we first prepare a centre frequency for frequency sweeping, which may be modified during analysis, to search for the dispersive shift +# note that dispersive shift and lamb shift depends on detuning(i.e.: drive_frequency - bare_resonator_frequency) +# lamb shifted frequncy would then be shifted dispersively depending on ground_state or excited state of qubit +# fitting of |S21| is discussed here: https://github.com/qiboteam/qibocal/pull/917 span = 1e6 center_frequency = bare_resonator_frequency freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) @@ -54,12 +54,11 @@ freq_sweep *= 1e9 - -#demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator; (for codomain of [-pi/2,pi/2]) -#note that we can always shift the phase/angle by pi, as a result of arctan(), as there are two angles resulting in the same tan() value -#this means that we can shift the positive angles downwards by subtracting them with pi, resulting in codomain of [0,-2pi] -#for a clearer picture (using codomain of [0,-2pi] @see https://arxiv.org/pdf/1904.06560), we can see that -#the phase response of resonator shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies. +# demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator; (for codomain of [-pi/2,pi/2]) +# note that we can always shift the phase/angle by pi, as a result of arctan(), as there are two angles resulting in the same tan() value +# this means that we can shift the positive angles downwards by subtracting them with pi, resulting in codomain of [0,-2pi] +# for a clearer picture (using codomain of [0,-2pi] @see https://arxiv.org/pdf/1904.06560), we can see that +# the phase response of resonator shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies. y_gnd1 = np.angle(readout.ground_s21(freq_sweep)) y_exc1 = np.angle(readout.excited_s21(freq_sweep)) freq_sweep /= 1e9 @@ -74,12 +73,11 @@ plt.show() - -#demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane -#we prepare a lambshifted readout pulse frequency according to the first and second demonstration, -#so that we can inspect the phase response of resonator (being plotted on V_I/V_Q plane), -#which shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies. -#in other words, the data probed from resonator for particular qubit states should be well separated as demonstrated previously +# demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane +# we prepare a lambshifted readout pulse frequency according to the first and second demonstration, +# so that we can inspect the phase response of resonator (being plotted on V_I/V_Q plane), +# which shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies. +# in other words, the data probed from resonator for particular qubit states should be well separated as demonstrated previously ro_frequency = 5.0450e9 + readout.lambshift ro_pulse = ReadoutPulse( start=0, @@ -95,7 +93,7 @@ plt.scatter(np.real(rgnd), np.imag(rgnd), label=r"$|0\rangle$") plt.scatter(np.real(rexc), np.imag(rexc), label=r"$|1\rangle$") # when we set NOISE_AMP to zero, using the follow axes limits allow us to see the maximally separated data -# plt.xlim([0,1]) +# plt.xlim([0,1]) # plt.ylim([-1,1]) plt.xlabel(r"$V_I$") plt.ylabel(r"$V_Q$") diff --git a/src/qibolab/instruments/emulator/readout.py b/src/qibolab/instruments/emulator/readout.py index 57fab9da5e..1302c98c7c 100644 --- a/src/qibolab/instruments/emulator/readout.py +++ b/src/qibolab/instruments/emulator/readout.py @@ -23,7 +23,7 @@ def lamb_shift(g, delta): def dispersive_shift(g, delta, alpha): """Calculates the dispersive shift of the readout resonator for depending on the state of the qubit @see https://arxiv.org/pdf/1904.06560, equation 146, negative sign is omitted as it is included in the definition of delta - a factor of two is added for better approximation of raw data, comparing with equation 35 from https://arxiv.org/pdf/2106.06173 + a factor of two is added for better approximation of raw data, comparing with equation 35 from https://arxiv.org/pdf/2106.06173 Args: g (float): Coupling strength between readout resonator and qubit in Hz. @@ -33,7 +33,7 @@ def dispersive_shift(g, delta, alpha): Returns: chi (float): Dispersive shift in Hz. """ - return 2 * g *g /delta *(1/(1+(delta/alpha))) + return 2 * g * g / delta * (1 / (1 + (delta / alpha))) def s21_function(resonator_frequency, total_Q, coupling_Q): @@ -66,13 +66,21 @@ def __init__( coupling_Q (float): Coupling/external Q factor of the readout resonator. sampling_rate (float): Sampling rate of the ADC/digitizer. """ - #maintaining the definition of |0> = |e> = (1 0) with the JC Hamiltonian model - #ground_state_frequency = dressed resonator frequency when qubit is in ground state (vice versa for excited_state_frequency) + # maintaining the definition of |0> = |e> = (1 0) with the JC Hamiltonian model + # ground_state_frequency = dressed resonator frequency when qubit is in ground state (vice versa for excited_state_frequency) delta = qubit.drive_frequency - qubit.bare_resonator_frequency - ground_state_frequency = qubit.bare_resonator_frequency - lamb_shift(g,delta) - dispersive_shift(g, delta, qubit.anharmonicity) - excited_state_frequency = qubit.bare_resonator_frequency - lamb_shift(g,delta) + dispersive_shift(g, delta, qubit.anharmonicity) - - self.lambshift = -lamb_shift(g,delta) + ground_state_frequency = ( + qubit.bare_resonator_frequency + - lamb_shift(g, delta) + - dispersive_shift(g, delta, qubit.anharmonicity) + ) + excited_state_frequency = ( + qubit.bare_resonator_frequency + - lamb_shift(g, delta) + + dispersive_shift(g, delta, qubit.anharmonicity) + ) + + self.lambshift = -lamb_shift(g, delta) self.noise_model = noise_model self.sampling_rate = sampling_rate @@ -118,16 +126,20 @@ def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse): self.sampling_rate / 1e9 ) # Gigasample per second - start = int(pulse.start * 1e-9 * self.sampling_rate) #n = gigasample index - t = np.arange(start, start + len(env_I)) / self.sampling_rate #t_n - - #Low-pass filtered I-component (with intermediate frequency = carrier frequency) - i_filtered = reflected_amplitude*np.cos(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) - #Low-pass filtered Q-component - q_filtered = reflected_amplitude*np.sin(2*np.pi*t*pulse.frequency+ pulse.relative_phase + reflected_phase) + self.noise_model(t) - - z = i_filtered+1j*q_filtered - z *= np.exp(-1j*2*np.pi*t*pulse.frequency) - z = np.sum(z)/len(t) + start = int(pulse.start * 1e-9 * self.sampling_rate) # n = gigasample index + t = np.arange(start, start + len(env_I)) / self.sampling_rate # t_n + + # Low-pass filtered I-component (with intermediate frequency = carrier frequency) + i_filtered = reflected_amplitude * np.cos( + 2 * np.pi * t * pulse.frequency + pulse.relative_phase + reflected_phase + ) + self.noise_model(t) + # Low-pass filtered Q-component + q_filtered = reflected_amplitude * np.sin( + 2 * np.pi * t * pulse.frequency + pulse.relative_phase + reflected_phase + ) + self.noise_model(t) + + z = i_filtered + 1j * q_filtered + z *= np.exp(-1j * 2 * np.pi * t * pulse.frequency) + z = np.sum(z) / len(t) return z From 60c0f0469bb77e1d52b575b547c5bf24b73e003d Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Mon, 16 Sep 2024 18:24:16 +0800 Subject: [PATCH 09/18] added LO frequency option, restored envelope waveform --- readout_example/IQ.png | Bin 73928 -> 96364 bytes readout_example/readout_example.py | 19 +++--- src/qibolab/instruments/emulator/readout.py | 61 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zfJ(mW{oOu@8q}zP69vqk9=!&k(hJI7 zlnYQYnhm!ibZ~KS_(L)H4#e4+Cj?*+?gAJzglH2%K&LY`IW{unDYPZsgV$lZl}qaM1ws~p=aJ+s2RMUQOUnu3~DZDSFU(N$KkV?Un9HP8@@*bBFa-} z`{JHQw=Pt<)QLUtJSnle?HGX%hWGvrU4w|1>RdxyD2m6<)i84sE8fsw94Um%-_8`E zMlS?iyu5&`yo+cEfNQ$%vr&f1_tN2qTiv>xZa=%C4ydMD}=>!}s{z*rS5yY*byVM`}KKI<?b$PnXK)PqP-PR0*kOhQjp-b0 zkB7p)X5cY+Qu8`+<;H(Df{(d4={Gp%dqJswC=AZUZyGO09LNUW=H$Iy) zN4+=jRg{L?`vXj{cw}x$-9falFO(27x+bCb4iRi#UQ;k4KwbszpJB~^J^q|d<=!9G z*p2?DVB5_z53rAuqi8HhC}wvwP_DlFVmuB$J*RTFaqE-(Va6NRy?w;X+&&sEf~nuj z(^g)P^L9@UtnR>q)`;yc=)+{l1y4YtowkGrC{h7UZ#t89{X1YecxGcah(9w|)!8p} z4(%e8-#f>2{~C+(ZiK_Bs_r2no*$Up6un6|Xn{F@E&(PQ@u(gD&t8L8J=QGJ@SL<{ zWi!a*v!>SrCu+d=ia{3N0!Ji$Wq_B&I3yZ?i>izgu#Xgd7X_I!Ey`agvt;?-`lK!;2JDFgbD!DJO^B@l4IfvBJ$ zuyYPB=1wSq^AAuKxG2m$2OJw9COBJw3~d}?H6J7-M!gRT2a;mH Date: Mon, 16 Sep 2024 10:26:07 +0000 Subject: [PATCH 10/18] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- readout_example/readout_example.py | 4 +-- src/qibolab/instruments/emulator/readout.py | 37 ++++++++++++--------- 2 files changed, 23 insertions(+), 18 deletions(-) diff --git a/readout_example/readout_example.py b/readout_example/readout_example.py index 1a6c142af4..a0d5f9c261 100644 --- a/readout_example/readout_example.py +++ b/readout_example/readout_example.py @@ -1,7 +1,7 @@ import matplotlib.pyplot as plt import numpy as np -from qibolab.instruments.emulator.readout import ReadoutSimulator,lamb_shift +from qibolab.instruments.emulator.readout import ReadoutSimulator, lamb_shift from qibolab.pulses import ReadoutPulse from qibolab.qubits import Qubit @@ -79,7 +79,7 @@ # which shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies. # in other words, the data probed from resonator for particular qubit states should be well separated as demonstrated previously delta = qb.drive_frequency - qb.bare_resonator_frequency -ro_frequency = 5.0450e9 - lamb_shift(g=10e6,delta=delta) +ro_frequency = 5.0450e9 - lamb_shift(g=10e6, delta=delta) ro_pulse = ReadoutPulse( start=0, duration=1000, diff --git a/src/qibolab/instruments/emulator/readout.py b/src/qibolab/instruments/emulator/readout.py index a1111af8e4..9ba2e11dc3 100644 --- a/src/qibolab/instruments/emulator/readout.py +++ b/src/qibolab/instruments/emulator/readout.py @@ -96,7 +96,7 @@ def simulate_ground_state_iq(self, pulse: ReadoutPulse, LO_frequency=None): """ if LO_frequency: intermediate_frequency = np.abs(pulse.frequency - LO_frequency) - else: + else: intermediate_frequency = pulse.frequency s21 = self.ground_s21(intermediate_frequency) @@ -111,13 +111,15 @@ def simulate_excited_state_iq(self, pulse: ReadoutPulse, LO_frequency=None): """ if LO_frequency: intermediate_frequency = np.abs(pulse.frequency - LO_frequency) - else: + else: intermediate_frequency = pulse.frequency - + s21 = self.excited_s21(intermediate_frequency) return self.simulate_and_demodulate(s21, pulse, intermediate_frequency) - def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse, intermediate_frequency=None): + def simulate_and_demodulate( + self, s21: complex, pulse: ReadoutPulse, intermediate_frequency=None + ): """Simulates the readout pulse for a given S21-parameter and homodyne demodulation/2nd stage of heterodyne demodulation. @@ -139,38 +141,41 @@ def simulate_and_demodulate(self, s21: complex, pulse: ReadoutPulse, intermediat t = np.arange(start, start + len(env_I)) / self.sampling_rate # t_n reference_signal_I = np.sin( - 2 * np.pi * intermediate_frequency * t + pulse.relative_phase) + 2 * np.pi * intermediate_frequency * t + pulse.relative_phase + ) reference_signal_Q = np.cos( - 2 * np.pi * intermediate_frequency * t + pulse.relative_phase) + 2 * np.pi * intermediate_frequency * t + pulse.relative_phase + ) envelope_waveform = ( reflected_amplitude * pulse.amplitude * ( env_I.data * np.cos(pulse.relative_phase + reflected_phase) - * np.sin(2 * np.pi * intermediate_frequency* t) + * np.sin(2 * np.pi * intermediate_frequency * t) + env_Q.data * np.sin(pulse.relative_phase + reflected_phase) * np.cos(2 * np.pi * intermediate_frequency * t) - )) + ) + ) i_envelope = np.dot(envelope_waveform, reference_signal_I) q_envelope = np.dot(envelope_waveform, reference_signal_Q) - # Low-pass filtered I-component (with intermediate frequency = pulse frequency if LO is absent) - i_filtered = i_envelope* np.cos( - 2 * np.pi * t * intermediate_frequency + pulse.relative_phase + reflected_phase + i_filtered = i_envelope * np.cos( + 2 * np.pi * t * intermediate_frequency + + pulse.relative_phase + + reflected_phase ) + self.noise_model(t) # Low-pass filtered Q-component - q_filtered = q_envelope* np.sin( - 2 * np.pi * t * intermediate_frequency + pulse.relative_phase + reflected_phase + q_filtered = q_envelope * np.sin( + 2 * np.pi * t * intermediate_frequency + + pulse.relative_phase + + reflected_phase ) + self.noise_model(t) - z = i_filtered + 1j * q_filtered z *= np.exp(-1j * 2 * np.pi * t * intermediate_frequency) z = np.sum(z) / len(t) return z - - \ No newline at end of file From 86db30714138dcf38080243677bd94eb3c632bff Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Tue, 8 Oct 2024 17:10:00 +0800 Subject: [PATCH 11/18] integrated emulator readout module into pulse_simulator, to have readout resonator system --- readout_example/IQ_pulse_simulator.png | Bin 0 -> 122857 bytes .../IQ_pulse_simulator_zero_noise.png | Bin 0 -> 48273 bytes readout_example/{IQ.png => IQ_readout.png} | Bin readout_example/pulse_simulator_readout.py | 68 ++++++++++++++++++ src/qibolab/execution_parameters.py | 15 ++++ .../instruments/emulator/pulse_simulator.py | 63 ++++++++++++++++ .../ibmfakebelem_q01/parameters.json | 2 + 7 files changed, 148 insertions(+) create mode 100644 readout_example/IQ_pulse_simulator.png create mode 100644 readout_example/IQ_pulse_simulator_zero_noise.png rename readout_example/{IQ.png => IQ_readout.png} (100%) create mode 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ztrGaIa6<5xQ)Gt-x-Y!JG$A<$UD)v+B=(j4b^3 i3knPJe}4^|$9{KCyCUPv(6jtPU#wiG7r)%*>;C}STr?U0 literal 0 HcmV?d00001 diff --git a/readout_example/IQ.png b/readout_example/IQ_readout.png similarity index 100% rename from readout_example/IQ.png rename to readout_example/IQ_readout.png diff --git a/readout_example/pulse_simulator_readout.py b/readout_example/pulse_simulator_readout.py new file mode 100644 index 0000000000..f7710b9d91 --- /dev/null +++ b/readout_example/pulse_simulator_readout.py @@ -0,0 +1,68 @@ +import os + +import matplotlib.pyplot as plt +import numpy as np + +import qibolab +from qibolab.pulses import PulseSequence + +os.environ["QIBOLAB_PLATFORMS"] = ( + r"C:\Users\LexNg\OneDrive - Nanyang Technological University\YEAR 4 - FYP\qibolab\qibolab\tests\emulators" +) +# os.environ["QIBOLAB_PLATFORMS"] = r"C:\Users\pault\Documents\repos\qibolab\tests\emulators" +platform = qibolab.create_platform("ibmfakebelem_q01") + +pi_pulse = platform.create_RX_pulse(0) +readout_pulse = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse.finish) +# #for two readout pulses +# pi_pulse2 = platform.create_RX90_pulse(0, start = readout_pulse.finish) +# readout_pulse2 = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse2.finish) + +SNR = 30 # dB +NOISE_AMP = np.power(10, -SNR / 20) +AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 + +ps = PulseSequence(*[readout_pulse]) +opts = qibolab.ExecutionParameters( + nshots=100, + relaxation_time=100e3, + acquisition_type=qibolab.AcquisitionType.DISCRIMINATION, + averaging_mode=qibolab.AveragingMode.SINGLESHOT, + readout_simulator_config=dict( + g=10e6, + noise_model=AWGN, + internal_Q=2.5e6, + coupling_Q=6e4, + sampling_rate=1966.08e6, + ), +) + +gnd = platform.execute_pulse_sequence(ps, opts) +print(gnd) +print(gnd[0].probability()) + +ps.add(pi_pulse) +excited = platform.execute_pulse_sequence(ps, opts) +print(excited[0].probability()) + +i_ground = gnd["demodulation"][0]["i"] +q_ground = gnd["demodulation"][0]["q"] + +i_excited = excited["demodulation"][0]["i"] +q_excited = excited["demodulation"][0]["q"] + +# evenly distributed as measurement of ground state(excited state) results in +# excited state(ground state) due to noise and statistical fluctuations in measurement +plt.scatter(i_ground, q_ground, label=r"$|0\rangle$") +plt.scatter(i_excited, q_excited, label=r"$|1\rangle$") + +plt.xlabel(r"$V_I$ [a.u.]") +plt.ylabel(r"$V_Q$ [a.u.]") + +# #when scale in noise_model = 0 +# plt.xlim([0,0.8]) + +plt.legend() +plt.tight_layout() +# plt.savefig("IQ_pulse_simulator.png", dpi=300) +plt.show() diff --git a/src/qibolab/execution_parameters.py b/src/qibolab/execution_parameters.py index b317caf138..f959db7688 100644 --- a/src/qibolab/execution_parameters.py +++ b/src/qibolab/execution_parameters.py @@ -73,6 +73,21 @@ class ExecutionParameters: averaging_mode: AveragingMode = AveragingMode.SINGLESHOT """Data averaging mode.""" + readout_simulator_config: Optional[dict] = None + """Dict(g=, noise_model=, internal_Q=, coupling_Q=, sampling_rate=) + Additional parameters needed for ReadoutSimulator for demodulation + emulation. + + Example of noise_model: + SNR = 30 # dB + NOISE_AMP = np.power(10, -SNR / 20) + AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 + + noise_model = AWGN + + **initialize scale=0 if noise_model is not needed + """ + @property def results_type(self): """Returns corresponding results class.""" diff --git a/src/qibolab/instruments/emulator/pulse_simulator.py b/src/qibolab/instruments/emulator/pulse_simulator.py index 07e40aceb7..9d601071a1 100644 --- a/src/qibolab/instruments/emulator/pulse_simulator.py +++ b/src/qibolab/instruments/emulator/pulse_simulator.py @@ -12,6 +12,7 @@ from qibolab.instruments.abstract import Controller from qibolab.instruments.emulator.engines.qutip_engine import QutipSimulator from qibolab.instruments.emulator.models import general_no_coupler_model +from qibolab.instruments.emulator.readout import ReadoutSimulator from qibolab.pulses import PulseSequence, PulseType, ReadoutPulse from qibolab.qubits import Qubit, QubitId from qibolab.result import IntegratedResults, SampleResults @@ -180,8 +181,70 @@ def play( "output_states": output_states, } + results["demodulation"] = {} + + if execution_parameters.acquisition_type is AcquisitionType.DISCRIMINATION: + if execution_parameters.readout_simulator_config: + for index, ro_pulse in enumerate(ro_pulse_list): + # obtain qubit with its qubitID + qubit = qubits[self.get_qubit(ro_pulse.qubit, qubits)] + # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency, + # such that the V_I/V_Q are maximally seperated (i.e.: bare_resonator_frequency = lambshifted readout pulse frequency + lamb shift) + # solve quadratic equation for bare_resonator frequency + g = execution_parameters.readout_simulator_config["g"] + noise_model = execution_parameters.readout_simulator_config[ + "noise_model" + ] + internal_Q = execution_parameters.readout_simulator_config[ + "internal_Q" + ] + coupling_Q = execution_parameters.readout_simulator_config[ + "coupling_Q" + ] + sampling_rate = execution_parameters.readout_simulator_config[ + "sampling_rate" + ] + readout = ReadoutSimulator( + qubit=qubit, + g=g, + noise_model=noise_model, + internal_Q=internal_Q, + coupling_Q=coupling_Q, + sampling_rate=sampling_rate, + ) + + values = np.array(samples[ro_pulse.qubit]) + for i in range(len(values)): + values = values.astype(np.complex128) + if values[i] == 0: + values[i] = readout.simulate_ground_state_iq(ro_pulse) + elif values[i] == 1: + values[i] = readout.simulate_excited_state_iq(ro_pulse) + else: + raise ValueError("measurement output is not 0 or 1") + + # z is the complex readout wave with z = i_filtered + 1j * q_filtered + # i is the index of readout pulse, e.g.: first or second readout in a single shot + results["demodulation"][index] = { + "z": values, + "i": np.real(values), + "q": np.imag(values), + } + return results + def get_qubit(self, target_qubit, qubits): + """Return the name of target physical qubit (qubitID) corresponding to + a logical qubit. + + Temporary fix for the compiler to work for platforms where the + qubits are not named as 0, 1, 2, ... + """ + try: + return qubits[target_qubit].name + except KeyError: + return list(qubits.keys())[target_qubit] + ### sweeper adapted from icarusqfpga ### def sweep( self, diff --git a/tests/emulators/ibmfakebelem_q01/parameters.json b/tests/emulators/ibmfakebelem_q01/parameters.json index 39ef172066..dc3a8c75fd 100644 --- a/tests/emulators/ibmfakebelem_q01/parameters.json +++ b/tests/emulators/ibmfakebelem_q01/parameters.json @@ -252,6 +252,7 @@ "characterization": { "single_qubit": { "0": { + "bare_resonator_frequency": 7301616604.7871, "readout_frequency": 7301661824.000001, "drive_frequency": 5090167234.445013, "anharmonicity": -336123005.1821652, @@ -267,6 +268,7 @@ "iq_angle": 2.602 }, "1": { + "bare_resonator_frequency": 7393381494, "readout_frequency": 7393428047.0, "drive_frequency": 5245306068.285917, "anharmonicity": -316572131.412737, From 122c5178a0e9ddbe631abdbeb0d52825f05f2d9b Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Tue, 8 Oct 2024 22:18:36 +0800 Subject: [PATCH 12/18] provided notebook tutorials on both readout module and PulseSimulator readout, amended code structure for configuration of ReadoutSimulator --- .../pulse_simulator_readout.ipynb | 307 ++++++++++++++++++ .../pulse_simulator_readout.py | 33 +- .../emulator readout/readout_example.ipynb | 228 +++++++++++++ .../emulator readout}/readout_example.py | 0 readout_example/IQ_pulse_simulator.png | Bin 122857 -> 0 bytes .../IQ_pulse_simulator_zero_noise.png | Bin 48273 -> 0 bytes readout_example/IQ_readout.png | Bin 96364 -> 0 bytes readout_example/S21.png | Bin 113299 -> 0 bytes readout_example/phase.png | Bin 117941 -> 0 bytes src/qibolab/execution_parameters.py | 15 - .../instruments/emulator/pulse_simulator.py | 90 ++--- .../ibmfakebelem_q01/parameters.json | 7 + 12 files changed, 598 insertions(+), 82 deletions(-) create mode 100644 examples/emulator readout/pulse_simulator_readout.ipynb rename {readout_example => examples/emulator readout}/pulse_simulator_readout.py (59%) create mode 100644 examples/emulator readout/readout_example.ipynb rename {readout_example => examples/emulator readout}/readout_example.py (100%) delete mode 100644 readout_example/IQ_pulse_simulator.png delete mode 100644 readout_example/IQ_pulse_simulator_zero_noise.png delete mode 100644 readout_example/IQ_readout.png delete mode 100644 readout_example/S21.png delete mode 100644 readout_example/phase.png diff --git a/examples/emulator readout/pulse_simulator_readout.ipynb b/examples/emulator readout/pulse_simulator_readout.ipynb new file mode 100644 index 0000000000..dd34bb0107 --- /dev/null +++ b/examples/emulator readout/pulse_simulator_readout.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PulseSimulator readout tutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import qibolab\n", + "from qibolab.pulses import PulseSequence\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's set up the platform." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.11|INFO|2024-10-08 22:15:42]: Loading platform ibmfakebelem_q01\n", + "INFO:qibo.config:Loading platform ibmfakebelem_q01\n" + ] + } + ], + "source": [ + "os.environ[\"QIBOLAB_PLATFORMS\"] = (r\"..\\..\\tests\\emulators\")\n", + "\n", + "platform = qibolab.create_platform(\"ibmfakebelem_q01\")\n", + "\n", + "pi_pulse = platform.create_RX_pulse(0)\n", + "readout_pulse = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse.finish)\n", + "# #for two readout pulses\n", + "# pi_pulse2 = platform.create_RX90_pulse(0, start = readout_pulse.finish)\n", + "# readout_pulse2 = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse2.finish)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's initialize the required parameters for readout setup. \n", + "\n", + "- Note that the `readout_simulator_config` consisting of the parameters for ReadoutSimulator shall be included/initialized in runcard." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "ps = PulseSequence(*[readout_pulse])\n", + "opts = qibolab.ExecutionParameters(\n", + " nshots=100,\n", + " relaxation_time=100e3,\n", + " acquisition_type=qibolab.AcquisitionType.INTEGRATION,\n", + " averaging_mode=qibolab.AveragingMode.SINGLESHOT,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To obtain the measurement outcome, first let's look at `gnd` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.11|INFO|2024-10-08 22:15:42]: Minimal execution time (sequence): 0.012240000000000001\n", + "INFO:qibo.config:Minimal execution time (sequence): 0.012240000000000001\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 0.01s ] Elapsed 0.01s / Remaining 00:00:00:00\n", + "This is gnd object:\n", + " {0: , 'ReadoutPulse(61.117, 22400, 0.03, 7_301_661_824.000001, 0, Rectangular(), readout-0, 0)': , 'simulation': {'sequence_duration': 13.777777777777777, 'simulation_dt': 13.6, 'simulation_time': 0.020655000116676092, 'output_states': [Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", + "Qobj data =\n", + "[[1. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]]}}\n", + "This is demodulation data from gnd object:\n", + " [ 6.05278231 -6.77345759j 7.38630837 -1.69642791j\n", + " -5.66006861 +3.39606537j -2.26870633 -1.36192903j\n", + " 2.06117258 +6.56490528j 0.29225381 -1.739851j\n", + " -9.42817116 -2.39380988j 9.58050566 -0.45125213j\n", + " 3.3725004 -0.98323924j 7.49392067 -4.34147872j\n", + " -2.36569562-16.01870746j -2.48347485 -4.68041209j\n", + " 1.17744038 -7.30566632j 2.17156053 +0.31478218j\n", + " 1.5050078 +1.03443056j -2.57982937 +4.22348515j\n", + " -2.54312492+11.94309056j 8.95867216 -5.75630801j\n", + " -0.8852933 -8.05608845j 0.57395834 +0.67901474j\n", + " 0.16186509 -5.79164452j -2.51891768 -0.07879709j\n", + " -2.03464108 +5.16774506j 0.94293826 -0.03913329j\n", + " 4.98885527 -1.03688984j 1.29115901 -2.60703985j\n", + " 0.39263835 -1.90406852j -2.87572394 +4.24168539j\n", + " -2.60468331 +5.10453648j -1.82729099 +1.13724587j\n", + " -5.31951359 +5.73847885j 3.43148303 -4.33749466j\n", + " 1.58605728 +7.76792855j 6.24774873 +6.27408945j\n", + " 2.16991705 +0.77763338j 0.44573264 +0.54137837j\n", + " 4.35325445 -1.53401159j 4.87352671 -1.34403974j\n", + " 4.23573334 -4.05893376j -3.79229181 +6.29479422j\n", + " -5.05042129 +1.6307828j 2.37895156 +2.84668426j\n", + " -4.17064035 -4.17335119j 2.52654653 -0.54613496j\n", + " 1.48567341 +0.73796774j 1.62535946 +3.51125586j\n", + " 4.36057407 -1.75856664j -3.7239906 -4.22574389j\n", + " -6.1196262 +0.21730615j 0.68007579 -7.25111195j\n", + " 4.44108791 -5.65876725j -3.7071624 -3.50561215j\n", + " -7.96252005 +0.85290827j 5.23014424 -2.85446077j\n", + " 7.37050611-10.32418612j 4.94142221 +5.95418745j\n", + " -5.1532398 +4.71934181j 1.31847131 +1.86742721j\n", + " 7.58774986-11.92224307j 2.95842367 -4.94351906j\n", + " 0.17459141 -3.04377556j 5.22981905 -1.82870018j\n", + " -3.8350977 -1.39612341j -1.21955233 -0.758712j\n", + " -0.77822018 -5.57824439j 1.78063699 +5.88075131j\n", + " -9.05632175 +2.90022535j 1.38797379 +0.4229052j\n", + " 1.60056263-10.53461858j -4.87884425 -0.45226876j\n", + " 2.06680891 -2.70351975j 3.07355014 -2.55335899j\n", + " 4.3712963 +0.50313208j -0.53919979 +0.48105931j\n", + " -11.82440821 -3.38521069j 2.73515491 -7.49077715j\n", + " 2.51952353 -0.67272361j -5.0092172 -8.08234164j\n", + " -0.12094645 +4.32345655j -4.98392789 -2.83425444j\n", + " -0.59447876 +3.17073333j 6.59933921 +1.75034684j\n", + " -2.53537382 -6.20200851j 2.96632865 -5.55938496j\n", + " 1.00300714 -9.32277936j -3.80292587 -9.54057899j\n", + " -2.13386596 -2.24518363j -2.41425803 -2.64891419j\n", + " -4.25955902 -0.89612238j -4.99919545-14.02814984j\n", + " -2.08958543 +4.87547675j 0.90034582 +4.52328072j\n", + " 8.31582735 +2.88191975j -0.72369141 -6.45333215j\n", + " 0.65815237 -0.96488808j 1.63646119 +1.18370829j\n", + " 2.17585378+10.74548305j -1.11276111-11.64387995j\n", + " -3.77738043 -3.91052529j 4.70779395 +4.75347545j]\n" + ] + } + ], + "source": [ + "gnd = platform.execute_pulse_sequence(ps, opts)\n", + "print(\"This is gnd object:\\n\", gnd)\n", + "print(\"This is demodulation data from gnd object:\\n\", gnd[0].voltage)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's continue with introducing an RX pulse to excite the qubit, and plot the combination of results from `gnd` and `excited`:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Qibo 0.2.11|INFO|2024-10-08 22:15:43]: Minimal execution time (sequence): 0.0122461117\n", + "INFO:qibo.config:Minimal execution time (sequence): 0.0122461117\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total run time: 0.96s*] Elapsed 0.96s / Remaining 00:00:00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ps.add(pi_pulse)\n", + "excited = platform.execute_pulse_sequence(ps, opts)\n", + "\n", + "\n", + "i_ground = gnd[0].voltage_i\n", + "q_ground = gnd[0].voltage_q\n", + "\n", + "i_excited = excited[0].voltage_i \n", + "q_excited = excited[0].voltage_q \n", + "\n", + "# evenly distributed as measurement of ground state(excited state) results in\n", + "# excited state(ground state) due to noise and statistical fluctuations in measurement\n", + "plt.scatter(i_ground, q_ground, label=r\"$|0\\rangle$\")\n", + "plt.scatter(i_excited, q_excited, label=r\"$|1\\rangle$\")\n", + "\n", + "plt.xlabel(r\"$V_I$ [a.u.]\")\n", + "plt.ylabel(r\"$V_Q$ [a.u.]\")\n", + "\n", + "# #when scale in noise_model = 0\n", + "# plt.xlim([0,0.8])\n", + "\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "# plt.savefig(\"IQ_pulse_simulator.png\", dpi=300)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/readout_example/pulse_simulator_readout.py b/examples/emulator readout/pulse_simulator_readout.py similarity index 59% rename from readout_example/pulse_simulator_readout.py rename to examples/emulator readout/pulse_simulator_readout.py index f7710b9d91..d3724be10c 100644 --- a/readout_example/pulse_simulator_readout.py +++ b/examples/emulator readout/pulse_simulator_readout.py @@ -1,15 +1,12 @@ import os import matplotlib.pyplot as plt -import numpy as np import qibolab from qibolab.pulses import PulseSequence -os.environ["QIBOLAB_PLATFORMS"] = ( - r"C:\Users\LexNg\OneDrive - Nanyang Technological University\YEAR 4 - FYP\qibolab\qibolab\tests\emulators" -) -# os.environ["QIBOLAB_PLATFORMS"] = r"C:\Users\pault\Documents\repos\qibolab\tests\emulators" +os.environ["QIBOLAB_PLATFORMS"] = r"tests\emulators" + platform = qibolab.create_platform("ibmfakebelem_q01") pi_pulse = platform.create_RX_pulse(0) @@ -18,38 +15,28 @@ # pi_pulse2 = platform.create_RX90_pulse(0, start = readout_pulse.finish) # readout_pulse2 = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse2.finish) -SNR = 30 # dB -NOISE_AMP = np.power(10, -SNR / 20) -AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 - ps = PulseSequence(*[readout_pulse]) opts = qibolab.ExecutionParameters( nshots=100, relaxation_time=100e3, - acquisition_type=qibolab.AcquisitionType.DISCRIMINATION, + acquisition_type=qibolab.AcquisitionType.INTEGRATION, averaging_mode=qibolab.AveragingMode.SINGLESHOT, - readout_simulator_config=dict( - g=10e6, - noise_model=AWGN, - internal_Q=2.5e6, - coupling_Q=6e4, - sampling_rate=1966.08e6, - ), ) gnd = platform.execute_pulse_sequence(ps, opts) print(gnd) -print(gnd[0].probability()) +print(gnd[0].voltage) + ps.add(pi_pulse) excited = platform.execute_pulse_sequence(ps, opts) -print(excited[0].probability()) -i_ground = gnd["demodulation"][0]["i"] -q_ground = gnd["demodulation"][0]["q"] -i_excited = excited["demodulation"][0]["i"] -q_excited = excited["demodulation"][0]["q"] +i_ground = gnd[0].voltage_i +q_ground = gnd[0].voltage_q + +i_excited = excited[0].voltage_i +q_excited = excited[0].voltage_q # evenly distributed as measurement of ground state(excited state) results in # excited state(ground state) due to noise and statistical fluctuations in measurement diff --git a/examples/emulator readout/readout_example.ipynb b/examples/emulator readout/readout_example.ipynb new file mode 100644 index 0000000000..8c565766b0 --- /dev/null +++ b/examples/emulator readout/readout_example.ipynb @@ -0,0 +1,228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Readout Tutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from qibolab.instruments.emulator.readout import ReadoutSimulator, lamb_shift\n", + "from qibolab.pulses import ReadoutPulse\n", + "from qibolab.qubits import Qubit\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's initialize the required parameters for readout setup." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "bare_resonator_frequency = 5.045e9\n", + "nshots = 100\n", + "\n", + "SNR = 30 # dB\n", + "READOUT_AMPLITUDE = 1\n", + "NOISE_AMP = np.power(10, -SNR / 20)\n", + "AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4\n", + "\n", + "qb = Qubit(\n", + " 0,\n", + " bare_resonator_frequency=bare_resonator_frequency,\n", + " drive_frequency=3.99e9,\n", + " anharmonicity=-263e6,\n", + ")\n", + "readout = ReadoutSimulator(\n", + " qubit=qb,\n", + " g=10e6,\n", + " noise_model=AWGN,\n", + " internal_Q=2.5e6,\n", + " coupling_Q=6e4,\n", + " sampling_rate=1966.08e6,\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This part of the code demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator.\n", + "\n", + "- We first prepare a centre frequency for frequency sweeping, which may be modified during analysis, to search for the `dispersive shift`. \n", + "- Note that dispersive shift and lamb shift depends on detuning (i.e.: `delta` = drive_frequency - bare_resonator_frequency).\n", + "- Lamb shifted frequency would then be shifted dispersively depending on ground_state or excited state of qubit.\n", + "- Fitting of |S21| is discussed here: https://github.com/qiboteam/qibocal/pull/917" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "span = 1e6\n", + "center_frequency = bare_resonator_frequency\n", + "freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000)\n", + "y_gnd = np.abs(readout.ground_s21(freq_sweep))\n", + "y_exc = np.abs(readout.excited_s21(freq_sweep))\n", + "\n", + "freq_sweep /= 1e9\n", + "plt.plot(freq_sweep, y_gnd, label=r\"$|0\\rangle$\")\n", + "plt.plot(freq_sweep, y_exc, label=r\"$|1\\rangle$\")\n", + "plt.ylabel(\"|S21| [arb. units]\")\n", + "plt.xlabel(\"Readout Frequency [GHz]\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "# plt.savefig(\"S21.png\", dpi=300)\n", + "plt.show()\n", + "\n", + "#convert the frequency unit back to Hz\n", + "freq_sweep *= 1e9\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This part of code demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator. (for codomain of [-pi/2,pi/2])\n", + "\n", + "- Note that we can always shift the phase/angle by pi, as a result of `arctan()`, as there are two angles resulting in the same `tan()` value.\n", + "- This means that we can shift the positive angles downwards by subtracting them with pi, resulting in codomain of [0,-2pi].\n", + "- For a clearer picture (using codomain of [0,-2pi] @see https://arxiv.org/pdf/1904.06560), we can see that the phase response of resonator shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_gnd1 = np.angle(readout.ground_s21(freq_sweep))\n", + "y_exc1 = np.angle(readout.excited_s21(freq_sweep))\n", + "freq_sweep /= 1e9\n", + "plt.plot(freq_sweep, y_gnd1, label=r\"$|0\\rangle$\")\n", + "plt.plot(freq_sweep, y_exc1, label=r\"$|1\\rangle$\")\n", + "plt.ylabel(r\"$\\theta$ phase [rad]\")\n", + "plt.xlabel(\"Readout Frequency [GHz]\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "# plt.savefig(\"phase.png\", dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This part of the code demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane.\n", + "\n", + "- We prepare a lamb shifted readout pulse frequency according to the first and second demonstration,so that we can inspect the phase response of resonator (being plotted on V_I/V_Q plane), which shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies.\n", + "- In other words, the data probed from resonator for particular qubit states should be well separated as demonstrated previously" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "delta = qb.drive_frequency - qb.bare_resonator_frequency\n", + "ro_frequency = 5.0450e9 - lamb_shift(g=10e6, delta=delta)\n", + "ro_pulse = ReadoutPulse(\n", + " start=0,\n", + " duration=1000,\n", + " amplitude=READOUT_AMPLITUDE,\n", + " frequency=ro_frequency,\n", + " shape=\"Rectangular()\",\n", + " relative_phase=0,\n", + ")\n", + "\n", + "rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)]\n", + "rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)]\n", + "plt.scatter(np.real(rgnd), np.imag(rgnd), label=r\"$|0\\rangle$\")\n", + "plt.scatter(np.real(rexc), np.imag(rexc), label=r\"$|1\\rangle$\")\n", + "# when we set NOISE_AMP to zero, using the follow axes limits allow us to see the maximally separated data\n", + "# plt.xlim([0,250])\n", + "# plt.ylim([-250,250])\n", + "plt.xlabel(r\"$V_I$ [a.u.]\")\n", + "plt.ylabel(r\"$V_Q$ [a.u.]\")\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "# plt.savefig(\"IQ_readout.png\", dpi=300)\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/readout_example/readout_example.py b/examples/emulator readout/readout_example.py similarity index 100% rename from readout_example/readout_example.py rename to examples/emulator readout/readout_example.py diff --git a/readout_example/IQ_pulse_simulator.png b/readout_example/IQ_pulse_simulator.png deleted file mode 100644 index 355bf2ec8077d18c4d47896d0c9516637c12e162..0000000000000000000000000000000000000000 GIT binary patch literal 0 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averaging_mode: AveragingMode = AveragingMode.SINGLESHOT """Data averaging mode.""" - readout_simulator_config: Optional[dict] = None - """Dict(g=, noise_model=, internal_Q=, coupling_Q=, sampling_rate=) - Additional parameters needed for ReadoutSimulator for demodulation - emulation. - - Example of noise_model: - SNR = 30 # dB - NOISE_AMP = np.power(10, -SNR / 20) - AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 - - noise_model = AWGN - - **initialize scale=0 if noise_model is not needed - """ - @property def results_type(self): """Returns corresponding results class.""" diff --git a/src/qibolab/instruments/emulator/pulse_simulator.py b/src/qibolab/instruments/emulator/pulse_simulator.py index 9d601071a1..bd5d18c052 100644 --- a/src/qibolab/instruments/emulator/pulse_simulator.py +++ b/src/qibolab/instruments/emulator/pulse_simulator.py @@ -36,6 +36,11 @@ GHZ = 1e9 +# noise_model +SNR = 30 # dB +NOISE_AMP = np.power(10, -SNR / 20) +AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 + class PulseSimulator(Controller): """Runs quantum dynamics simulation of model of device. @@ -88,6 +93,26 @@ def setup(self, **kwargs): self.simulate_dissipation = simulation_config["simulate_dissipation"] self.output_state_history = simulation_config["output_state_history"] + try: + readout_simulator_config = kwargs["readout_simulator_config"] + readout_simulator_config["noise_model"] = AWGN + self.readout_simulator_config = readout_simulator_config + except KeyError: + self.readout_simulator_config = None + """readout_simulator_config = Dict(g=, noise_model=, internal_Q=, coupling_Q=, sampling_rate=) + Additional parameters needed for ReadoutSimulator for demodulation + emulation. + + Example of calibrated noise_model (found in examples/emulator readout in readout_example.ipynb or readout_example.py): + SNR = 30 # dB + NOISE_AMP = np.power(10, -SNR / 20) + AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 + + noise_model = AWGN + + **initialize scale=0 if noise_model is not needed + """ + def connect(self): pass @@ -181,38 +206,17 @@ def play( "output_states": output_states, } - results["demodulation"] = {} - - if execution_parameters.acquisition_type is AcquisitionType.DISCRIMINATION: - if execution_parameters.readout_simulator_config: + if execution_parameters.acquisition_type is AcquisitionType.INTEGRATION: + if self.readout_simulator_config is not None: for index, ro_pulse in enumerate(ro_pulse_list): # obtain qubit with its qubitID - qubit = qubits[self.get_qubit(ro_pulse.qubit, qubits)] + qubit = qubits[get_qubit(ro_pulse.qubit, qubits)] # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency, # such that the V_I/V_Q are maximally seperated (i.e.: bare_resonator_frequency = lambshifted readout pulse frequency + lamb shift) # solve quadratic equation for bare_resonator frequency - g = execution_parameters.readout_simulator_config["g"] - noise_model = execution_parameters.readout_simulator_config[ - "noise_model" - ] - internal_Q = execution_parameters.readout_simulator_config[ - "internal_Q" - ] - coupling_Q = execution_parameters.readout_simulator_config[ - "coupling_Q" - ] - sampling_rate = execution_parameters.readout_simulator_config[ - "sampling_rate" - ] readout = ReadoutSimulator( - qubit=qubit, - g=g, - noise_model=noise_model, - internal_Q=internal_Q, - coupling_Q=coupling_Q, - sampling_rate=sampling_rate, + qubit=qubit, **self.readout_simulator_config ) - values = np.array(samples[ro_pulse.qubit]) for i in range(len(values)): values = values.astype(np.complex128) @@ -223,28 +227,13 @@ def play( else: raise ValueError("measurement output is not 0 or 1") - # z is the complex readout wave with z = i_filtered + 1j * q_filtered - # i is the index of readout pulse, e.g.: first or second readout in a single shot - results["demodulation"][index] = { - "z": values, - "i": np.real(values), - "q": np.imag(values), - } + processed_values = IntegratedResults(values) + results[ro_pulse.qubit] = results[ro_pulse.serial] = ( + processed_values + ) return results - def get_qubit(self, target_qubit, qubits): - """Return the name of target physical qubit (qubitID) corresponding to - a logical qubit. - - Temporary fix for the compiler to work for platforms where the - qubits are not named as 0, 1, 2, ... - """ - try: - return qubits[target_qubit].name - except KeyError: - return list(qubits.keys())[target_qubit] - ### sweeper adapted from icarusqfpga ### def sweep( self, @@ -473,6 +462,19 @@ def merge_sweep_results( } +def get_qubit(target_qubit, qubits): + """Return the name of target physical qubit (qubitID) corresponding to a + logical qubit. + + Temporary fix for the compiler to work for platforms where the + qubits are not named as 0, 1, 2, ... + """ + try: + return qubits[target_qubit].name + except KeyError: + return list(qubits.keys())[target_qubit] + + def ps_to_waveform_dict( sequence: PulseSequence, platform_to_simulator_channels: dict, diff --git a/tests/emulators/ibmfakebelem_q01/parameters.json b/tests/emulators/ibmfakebelem_q01/parameters.json index dc3a8c75fd..8e4289773b 100644 --- a/tests/emulators/ibmfakebelem_q01/parameters.json +++ b/tests/emulators/ibmfakebelem_q01/parameters.json @@ -66,6 +66,13 @@ "waveforms": 1, "readout": 1, "instructions": 1 + }, + "readout_simulator_config": { + "g": 10e6, + "noise_model": 0, + "internal_Q": 2.5e6, + "coupling_Q": 6e4, + "sampling_rate": 1966.08e6 } } }, From a2f40d83f75aa3cf4d2055fb5818948eea13f039 Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Wed, 9 Oct 2024 13:08:19 +0800 Subject: [PATCH 13/18] made the requested changes --- .../pulse_simulator_readout.ipynb | 307 ------------------ .../pulse_simulator_readout.py | 55 ---- .../emulator readout/readout_example.ipynb | 228 ------------- examples/emulator readout/readout_example.py | 104 ------ .../instruments/emulator/pulse_simulator.py | 75 ++--- tests/emulators/ibmfakebelem_q01/platform.py | 10 + 6 files changed, 41 insertions(+), 738 deletions(-) delete mode 100644 examples/emulator readout/pulse_simulator_readout.ipynb delete mode 100644 examples/emulator readout/pulse_simulator_readout.py delete mode 100644 examples/emulator readout/readout_example.ipynb delete mode 100644 examples/emulator readout/readout_example.py diff --git a/examples/emulator readout/pulse_simulator_readout.ipynb b/examples/emulator readout/pulse_simulator_readout.ipynb deleted file mode 100644 index dd34bb0107..0000000000 --- a/examples/emulator readout/pulse_simulator_readout.ipynb +++ /dev/null @@ -1,307 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## PulseSimulator readout tutorial" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "import qibolab\n", - "from qibolab.pulses import PulseSequence\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's set up the platform." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.11|INFO|2024-10-08 22:15:42]: Loading platform ibmfakebelem_q01\n", - "INFO:qibo.config:Loading platform ibmfakebelem_q01\n" - ] - } - ], - "source": [ - "os.environ[\"QIBOLAB_PLATFORMS\"] = (r\"..\\..\\tests\\emulators\")\n", - "\n", - "platform = qibolab.create_platform(\"ibmfakebelem_q01\")\n", - "\n", - "pi_pulse = platform.create_RX_pulse(0)\n", - "readout_pulse = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse.finish)\n", - "# #for two readout pulses\n", - "# pi_pulse2 = platform.create_RX90_pulse(0, start = readout_pulse.finish)\n", - "# readout_pulse2 = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse2.finish)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's initialize the required parameters for readout setup. \n", - "\n", - "- Note that the `readout_simulator_config` consisting of the parameters for ReadoutSimulator shall be included/initialized in runcard." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "ps = PulseSequence(*[readout_pulse])\n", - "opts = qibolab.ExecutionParameters(\n", - " nshots=100,\n", - " relaxation_time=100e3,\n", - " acquisition_type=qibolab.AcquisitionType.INTEGRATION,\n", - " averaging_mode=qibolab.AveragingMode.SINGLESHOT,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To obtain the measurement outcome, first let's look at `gnd` object:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.11|INFO|2024-10-08 22:15:42]: Minimal execution time (sequence): 0.012240000000000001\n", - "INFO:qibo.config:Minimal execution time (sequence): 0.012240000000000001\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 0.01s ] Elapsed 0.01s / Remaining 00:00:00:00\n", - "This is gnd object:\n", - " {0: , 'ReadoutPulse(61.117, 22400, 0.03, 7_301_661_824.000001, 0, Rectangular(), readout-0, 0)': , 'simulation': {'sequence_duration': 13.777777777777777, 'simulation_dt': 13.6, 'simulation_time': 0.020655000116676092, 'output_states': [Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]], Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[1. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]]]}}\n", - "This is demodulation data from gnd object:\n", - " [ 6.05278231 -6.77345759j 7.38630837 -1.69642791j\n", - " -5.66006861 +3.39606537j -2.26870633 -1.36192903j\n", - " 2.06117258 +6.56490528j 0.29225381 -1.739851j\n", - " -9.42817116 -2.39380988j 9.58050566 -0.45125213j\n", - " 3.3725004 -0.98323924j 7.49392067 -4.34147872j\n", - " -2.36569562-16.01870746j -2.48347485 -4.68041209j\n", - " 1.17744038 -7.30566632j 2.17156053 +0.31478218j\n", - " 1.5050078 +1.03443056j -2.57982937 +4.22348515j\n", - " -2.54312492+11.94309056j 8.95867216 -5.75630801j\n", - " -0.8852933 -8.05608845j 0.57395834 +0.67901474j\n", - " 0.16186509 -5.79164452j -2.51891768 -0.07879709j\n", - " -2.03464108 +5.16774506j 0.94293826 -0.03913329j\n", - " 4.98885527 -1.03688984j 1.29115901 -2.60703985j\n", - " 0.39263835 -1.90406852j -2.87572394 +4.24168539j\n", - " -2.60468331 +5.10453648j -1.82729099 +1.13724587j\n", - " -5.31951359 +5.73847885j 3.43148303 -4.33749466j\n", - " 1.58605728 +7.76792855j 6.24774873 +6.27408945j\n", - " 2.16991705 +0.77763338j 0.44573264 +0.54137837j\n", - " 4.35325445 -1.53401159j 4.87352671 -1.34403974j\n", - " 4.23573334 -4.05893376j -3.79229181 +6.29479422j\n", - " -5.05042129 +1.6307828j 2.37895156 +2.84668426j\n", - " -4.17064035 -4.17335119j 2.52654653 -0.54613496j\n", - " 1.48567341 +0.73796774j 1.62535946 +3.51125586j\n", - " 4.36057407 -1.75856664j -3.7239906 -4.22574389j\n", - " -6.1196262 +0.21730615j 0.68007579 -7.25111195j\n", - " 4.44108791 -5.65876725j -3.7071624 -3.50561215j\n", - " -7.96252005 +0.85290827j 5.23014424 -2.85446077j\n", - " 7.37050611-10.32418612j 4.94142221 +5.95418745j\n", - " -5.1532398 +4.71934181j 1.31847131 +1.86742721j\n", - " 7.58774986-11.92224307j 2.95842367 -4.94351906j\n", - " 0.17459141 -3.04377556j 5.22981905 -1.82870018j\n", - " -3.8350977 -1.39612341j -1.21955233 -0.758712j\n", - " -0.77822018 -5.57824439j 1.78063699 +5.88075131j\n", - " -9.05632175 +2.90022535j 1.38797379 +0.4229052j\n", - " 1.60056263-10.53461858j -4.87884425 -0.45226876j\n", - " 2.06680891 -2.70351975j 3.07355014 -2.55335899j\n", - " 4.3712963 +0.50313208j -0.53919979 +0.48105931j\n", - " -11.82440821 -3.38521069j 2.73515491 -7.49077715j\n", - " 2.51952353 -0.67272361j -5.0092172 -8.08234164j\n", - " -0.12094645 +4.32345655j -4.98392789 -2.83425444j\n", - " -0.59447876 +3.17073333j 6.59933921 +1.75034684j\n", - " -2.53537382 -6.20200851j 2.96632865 -5.55938496j\n", - " 1.00300714 -9.32277936j -3.80292587 -9.54057899j\n", - " -2.13386596 -2.24518363j -2.41425803 -2.64891419j\n", - " -4.25955902 -0.89612238j -4.99919545-14.02814984j\n", - " -2.08958543 +4.87547675j 0.90034582 +4.52328072j\n", - " 8.31582735 +2.88191975j -0.72369141 -6.45333215j\n", - " 0.65815237 -0.96488808j 1.63646119 +1.18370829j\n", - " 2.17585378+10.74548305j -1.11276111-11.64387995j\n", - " -3.77738043 -3.91052529j 4.70779395 +4.75347545j]\n" - ] - } - ], - "source": [ - "gnd = platform.execute_pulse_sequence(ps, opts)\n", - "print(\"This is gnd object:\\n\", gnd)\n", - "print(\"This is demodulation data from gnd object:\\n\", gnd[0].voltage)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's continue with introducing an RX pulse to excite the qubit, and plot the combination of results from `gnd` and `excited`:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Qibo 0.2.11|INFO|2024-10-08 22:15:43]: Minimal execution time (sequence): 0.0122461117\n", - "INFO:qibo.config:Minimal execution time (sequence): 0.0122461117\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Total run time: 0.96s*] Elapsed 0.96s / Remaining 00:00:00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ps.add(pi_pulse)\n", - "excited = platform.execute_pulse_sequence(ps, opts)\n", - "\n", - "\n", - "i_ground = gnd[0].voltage_i\n", - "q_ground = gnd[0].voltage_q\n", - "\n", - "i_excited = excited[0].voltage_i \n", - "q_excited = excited[0].voltage_q \n", - "\n", - "# evenly distributed as measurement of ground state(excited state) results in\n", - "# excited state(ground state) due to noise and statistical fluctuations in measurement\n", - "plt.scatter(i_ground, q_ground, label=r\"$|0\\rangle$\")\n", - "plt.scatter(i_excited, q_excited, label=r\"$|1\\rangle$\")\n", - "\n", - "plt.xlabel(r\"$V_I$ [a.u.]\")\n", - "plt.ylabel(r\"$V_Q$ [a.u.]\")\n", - "\n", - "# #when scale in noise_model = 0\n", - "# plt.xlim([0,0.8])\n", - "\n", - "plt.legend()\n", - "plt.tight_layout()\n", - "# plt.savefig(\"IQ_pulse_simulator.png\", dpi=300)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/emulator readout/pulse_simulator_readout.py b/examples/emulator readout/pulse_simulator_readout.py deleted file mode 100644 index d3724be10c..0000000000 --- a/examples/emulator readout/pulse_simulator_readout.py +++ /dev/null @@ -1,55 +0,0 @@ -import os - -import matplotlib.pyplot as plt - -import qibolab -from qibolab.pulses import PulseSequence - -os.environ["QIBOLAB_PLATFORMS"] = r"tests\emulators" - -platform = qibolab.create_platform("ibmfakebelem_q01") - -pi_pulse = platform.create_RX_pulse(0) -readout_pulse = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse.finish) -# #for two readout pulses -# pi_pulse2 = platform.create_RX90_pulse(0, start = readout_pulse.finish) -# readout_pulse2 = platform.create_qubit_readout_pulse(qubit=0, start=pi_pulse2.finish) - -ps = PulseSequence(*[readout_pulse]) -opts = qibolab.ExecutionParameters( - nshots=100, - relaxation_time=100e3, - acquisition_type=qibolab.AcquisitionType.INTEGRATION, - averaging_mode=qibolab.AveragingMode.SINGLESHOT, -) - -gnd = platform.execute_pulse_sequence(ps, opts) -print(gnd) -print(gnd[0].voltage) - - -ps.add(pi_pulse) -excited = platform.execute_pulse_sequence(ps, opts) - - -i_ground = gnd[0].voltage_i -q_ground = gnd[0].voltage_q - -i_excited = excited[0].voltage_i -q_excited = excited[0].voltage_q - -# evenly distributed as measurement of ground state(excited state) results in -# excited state(ground state) due to noise and statistical fluctuations in measurement -plt.scatter(i_ground, q_ground, label=r"$|0\rangle$") -plt.scatter(i_excited, q_excited, label=r"$|1\rangle$") - -plt.xlabel(r"$V_I$ [a.u.]") -plt.ylabel(r"$V_Q$ [a.u.]") - -# #when scale in noise_model = 0 -# plt.xlim([0,0.8]) - -plt.legend() -plt.tight_layout() -# plt.savefig("IQ_pulse_simulator.png", dpi=300) -plt.show() diff --git a/examples/emulator readout/readout_example.ipynb b/examples/emulator readout/readout_example.ipynb deleted file mode 100644 index 8c565766b0..0000000000 --- a/examples/emulator readout/readout_example.ipynb +++ /dev/null @@ -1,228 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Readout Tutorial" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from qibolab.instruments.emulator.readout import ReadoutSimulator, lamb_shift\n", - "from qibolab.pulses import ReadoutPulse\n", - "from qibolab.qubits import Qubit\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's initialize the required parameters for readout setup." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "bare_resonator_frequency = 5.045e9\n", - "nshots = 100\n", - "\n", - "SNR = 30 # dB\n", - "READOUT_AMPLITUDE = 1\n", - "NOISE_AMP = np.power(10, -SNR / 20)\n", - "AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4\n", - "\n", - "qb = Qubit(\n", - " 0,\n", - " bare_resonator_frequency=bare_resonator_frequency,\n", - " drive_frequency=3.99e9,\n", - " anharmonicity=-263e6,\n", - ")\n", - "readout = ReadoutSimulator(\n", - " qubit=qb,\n", - " g=10e6,\n", - " noise_model=AWGN,\n", - " internal_Q=2.5e6,\n", - " coupling_Q=6e4,\n", - " sampling_rate=1966.08e6,\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This part of the code demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator.\n", - "\n", - "- We first prepare a centre frequency for frequency sweeping, which may be modified during analysis, to search for the `dispersive shift`. \n", - "- Note that dispersive shift and lamb shift depends on detuning (i.e.: `delta` = drive_frequency - bare_resonator_frequency).\n", - "- Lamb shifted frequency would then be shifted dispersively depending on ground_state or excited state of qubit.\n", - "- Fitting of |S21| is discussed here: https://github.com/qiboteam/qibocal/pull/917" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "span = 1e6\n", - "center_frequency = bare_resonator_frequency\n", - "freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000)\n", - "y_gnd = np.abs(readout.ground_s21(freq_sweep))\n", - "y_exc = np.abs(readout.excited_s21(freq_sweep))\n", - "\n", - "freq_sweep /= 1e9\n", - "plt.plot(freq_sweep, y_gnd, label=r\"$|0\\rangle$\")\n", - "plt.plot(freq_sweep, y_exc, label=r\"$|1\\rangle$\")\n", - "plt.ylabel(\"|S21| [arb. units]\")\n", - "plt.xlabel(\"Readout Frequency [GHz]\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.tight_layout()\n", - "# plt.savefig(\"S21.png\", dpi=300)\n", - "plt.show()\n", - "\n", - "#convert the frequency unit back to Hz\n", - "freq_sweep *= 1e9\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This part of code demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator. (for codomain of [-pi/2,pi/2])\n", - "\n", - "- Note that we can always shift the phase/angle by pi, as a result of `arctan()`, as there are two angles resulting in the same `tan()` value.\n", - "- This means that we can shift the positive angles downwards by subtracting them with pi, resulting in codomain of [0,-2pi].\n", - "- For a clearer picture (using codomain of [0,-2pi] @see https://arxiv.org/pdf/1904.06560), we can see that the phase response of resonator shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_gnd1 = np.angle(readout.ground_s21(freq_sweep))\n", - "y_exc1 = np.angle(readout.excited_s21(freq_sweep))\n", - "freq_sweep /= 1e9\n", - "plt.plot(freq_sweep, y_gnd1, label=r\"$|0\\rangle$\")\n", - "plt.plot(freq_sweep, y_exc1, label=r\"$|1\\rangle$\")\n", - "plt.ylabel(r\"$\\theta$ phase [rad]\")\n", - "plt.xlabel(\"Readout Frequency [GHz]\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.tight_layout()\n", - "# plt.savefig(\"phase.png\", dpi=300)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This part of the code demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane.\n", - "\n", - "- We prepare a lamb shifted readout pulse frequency according to the first and second demonstration,so that we can inspect the phase response of resonator (being plotted on V_I/V_Q plane), which shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies.\n", - "- In other words, the data probed from resonator for particular qubit states should be well separated as demonstrated previously" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "delta = qb.drive_frequency - qb.bare_resonator_frequency\n", - "ro_frequency = 5.0450e9 - lamb_shift(g=10e6, delta=delta)\n", - "ro_pulse = ReadoutPulse(\n", - " start=0,\n", - " duration=1000,\n", - " amplitude=READOUT_AMPLITUDE,\n", - " frequency=ro_frequency,\n", - " shape=\"Rectangular()\",\n", - " relative_phase=0,\n", - ")\n", - "\n", - "rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)]\n", - "rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)]\n", - "plt.scatter(np.real(rgnd), np.imag(rgnd), label=r\"$|0\\rangle$\")\n", - "plt.scatter(np.real(rexc), np.imag(rexc), label=r\"$|1\\rangle$\")\n", - "# when we set NOISE_AMP to zero, using the follow axes limits allow us to see the maximally separated data\n", - "# plt.xlim([0,250])\n", - "# plt.ylim([-250,250])\n", - "plt.xlabel(r\"$V_I$ [a.u.]\")\n", - "plt.ylabel(r\"$V_Q$ [a.u.]\")\n", - "plt.legend()\n", - "plt.tight_layout()\n", - "# plt.savefig(\"IQ_readout.png\", dpi=300)\n", - "plt.show()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/emulator readout/readout_example.py b/examples/emulator readout/readout_example.py deleted file mode 100644 index a0d5f9c261..0000000000 --- a/examples/emulator readout/readout_example.py +++ /dev/null @@ -1,104 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np - -from qibolab.instruments.emulator.readout import ReadoutSimulator, lamb_shift -from qibolab.pulses import ReadoutPulse -from qibolab.qubits import Qubit - -bare_resonator_frequency = 5.045e9 -nshots = 100 - -SNR = 30 # dB -READOUT_AMPLITUDE = 1 -NOISE_AMP = np.power(10, -SNR / 20) -AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 - -qb = Qubit( - 0, - bare_resonator_frequency=bare_resonator_frequency, - drive_frequency=3.99e9, - anharmonicity=-263e6, -) -readout = ReadoutSimulator( - qubit=qb, - g=10e6, - noise_model=AWGN, - internal_Q=2.5e6, - coupling_Q=6e4, - sampling_rate=1966.08e6, -) - - -# demonstrates effect of state dependent dispersive shift on amplitude of reflected microwave from resonator; -# we first prepare a centre frequency for frequency sweeping, which may be modified during analysis, to search for the dispersive shift -# note that dispersive shift and lamb shift depends on detuning(i.e.: drive_frequency - bare_resonator_frequency) -# lamb shifted frequncy would then be shifted dispersively depending on ground_state or excited state of qubit -# fitting of |S21| is discussed here: https://github.com/qiboteam/qibocal/pull/917 -span = 1e6 -center_frequency = bare_resonator_frequency -freq_sweep = np.linspace(center_frequency - span / 2, center_frequency + span / 2, 1000) -y_gnd = np.abs(readout.ground_s21(freq_sweep)) -y_exc = np.abs(readout.excited_s21(freq_sweep)) - -freq_sweep /= 1e9 -plt.plot(freq_sweep, y_gnd, label=r"$|0\rangle$") -plt.plot(freq_sweep, y_exc, label=r"$|1\rangle$") -plt.ylabel("|S21| [arb. units]") -plt.xlabel("Readout Frequency [GHz]") -plt.legend() -plt.grid() -plt.tight_layout() -# plt.savefig("S21.png", dpi=300) -plt.show() - -freq_sweep *= 1e9 - - -# demonstrates effect of state dependent dispersive shift on phase of reflected microwave from resonator; (for codomain of [-pi/2,pi/2]) -# note that we can always shift the phase/angle by pi, as a result of arctan(), as there are two angles resulting in the same tan() value -# this means that we can shift the positive angles downwards by subtracting them with pi, resulting in codomain of [0,-2pi] -# for a clearer picture (using codomain of [0,-2pi] @see https://arxiv.org/pdf/1904.06560), we can see that -# the phase response of resonator shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies. -y_gnd1 = np.angle(readout.ground_s21(freq_sweep)) -y_exc1 = np.angle(readout.excited_s21(freq_sweep)) -freq_sweep /= 1e9 -plt.plot(freq_sweep, y_gnd1, label=r"$|0\rangle$") -plt.plot(freq_sweep, y_exc1, label=r"$|1\rangle$") -plt.ylabel(r"$\theta$ phase [rad]") -plt.xlabel("Readout Frequency [GHz]") -plt.legend() -plt.grid() -plt.tight_layout() -# plt.savefig("phase.png", dpi=300) -plt.show() - - -# demonstrates the separation of V_I/V_Q data of reflected microwave on V_I/V_Q plane -# we prepare a lambshifted readout pulse frequency according to the first and second demonstration, -# so that we can inspect the phase response of resonator (being plotted on V_I/V_Q plane), -# which shall be maximally separated when resonator is probed just in-between two qubit-state dependent resonance frequencies. -# in other words, the data probed from resonator for particular qubit states should be well separated as demonstrated previously -delta = qb.drive_frequency - qb.bare_resonator_frequency -ro_frequency = 5.0450e9 - lamb_shift(g=10e6, delta=delta) -ro_pulse = ReadoutPulse( - start=0, - duration=1000, - amplitude=READOUT_AMPLITUDE, - frequency=ro_frequency, - shape="Rectangular()", - relative_phase=0, -) - -rgnd = [readout.simulate_ground_state_iq(ro_pulse) for k in range(nshots)] -rexc = [readout.simulate_excited_state_iq(ro_pulse) for k in range(nshots)] -plt.scatter(np.real(rgnd), np.imag(rgnd), label=r"$|0\rangle$") -plt.scatter(np.real(rexc), np.imag(rexc), label=r"$|1\rangle$") -# when we set NOISE_AMP to zero, using the follow axes limits allow us to see the maximally separated data -# plt.xlim([0,250]) -# plt.ylim([-250,250]) -plt.xlabel(r"$V_I$ [a.u.]") -plt.ylabel(r"$V_Q$ [a.u.]") -plt.legend() -plt.tight_layout() -# plt.savefig("IQ.png", dpi=300) -plt.show() diff --git a/src/qibolab/instruments/emulator/pulse_simulator.py b/src/qibolab/instruments/emulator/pulse_simulator.py index bd5d18c052..e07794e239 100644 --- a/src/qibolab/instruments/emulator/pulse_simulator.py +++ b/src/qibolab/instruments/emulator/pulse_simulator.py @@ -36,11 +36,6 @@ GHZ = 1e9 -# noise_model -SNR = 30 # dB -NOISE_AMP = np.power(10, -SNR / 20) -AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 - class PulseSimulator(Controller): """Runs quantum dynamics simulation of model of device. @@ -93,20 +88,15 @@ def setup(self, **kwargs): self.simulate_dissipation = simulation_config["simulate_dissipation"] self.output_state_history = simulation_config["output_state_history"] - try: - readout_simulator_config = kwargs["readout_simulator_config"] - readout_simulator_config["noise_model"] = AWGN - self.readout_simulator_config = readout_simulator_config - except KeyError: - self.readout_simulator_config = None - """readout_simulator_config = Dict(g=, noise_model=, internal_Q=, coupling_Q=, sampling_rate=) - Additional parameters needed for ReadoutSimulator for demodulation - emulation. - - Example of calibrated noise_model (found in examples/emulator readout in readout_example.ipynb or readout_example.py): - SNR = 30 # dB - NOISE_AMP = np.power(10, -SNR / 20) - AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 + self.readout_simulator_config = kwargs.get("readout_simulator_config", None) + """readout_simulator_config = Dict(g=, noise_model=, internal_Q=, + coupling_Q=, sampling_rate=) Additional parameters needed for + ReadoutSimulator for demodulation emulation. + + Example of calibrated noise_model (found in examples/emulator + readout in readout_example.ipynb or readout_example.py): SNR = + 30 # dB NOISE_AMP = np.power(10, -SNR / 20) AWGN = lambda t: + np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 noise_model = AWGN @@ -206,31 +196,28 @@ def play( "output_states": output_states, } - if execution_parameters.acquisition_type is AcquisitionType.INTEGRATION: - if self.readout_simulator_config is not None: - for index, ro_pulse in enumerate(ro_pulse_list): - # obtain qubit with its qubitID - qubit = qubits[get_qubit(ro_pulse.qubit, qubits)] - # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency, - # such that the V_I/V_Q are maximally seperated (i.e.: bare_resonator_frequency = lambshifted readout pulse frequency + lamb shift) - # solve quadratic equation for bare_resonator frequency - readout = ReadoutSimulator( - qubit=qubit, **self.readout_simulator_config - ) - values = np.array(samples[ro_pulse.qubit]) - for i in range(len(values)): - values = values.astype(np.complex128) - if values[i] == 0: - values[i] = readout.simulate_ground_state_iq(ro_pulse) - elif values[i] == 1: - values[i] = readout.simulate_excited_state_iq(ro_pulse) - else: - raise ValueError("measurement output is not 0 or 1") - - processed_values = IntegratedResults(values) - results[ro_pulse.qubit] = results[ro_pulse.serial] = ( - processed_values - ) + if ( + execution_parameters.acquisition_type is AcquisitionType.INTEGRATION + and self.readout_simulator_config is not None + ): + for ro_pulse in ro_pulse_list: + # obtain qubit with its qubitID + qubit = qubits[get_qubit(ro_pulse.qubit, qubits)] + # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency, + # such that the V_I/V_Q are maximally seperated (i.e.: bare_resonator_frequency = lambshifted readout pulse frequency + lamb shift) + # solve quadratic equation for bare_resonator frequency + readout = ReadoutSimulator(qubit=qubit, **self.readout_simulator_config) + values = np.array(samples[ro_pulse.qubit]).astype(np.complex128) + for i in range(len(values)): + if values[i] == 0: + values[i] = readout.simulate_ground_state_iq(ro_pulse) + elif values[i] == 1: + values[i] = readout.simulate_excited_state_iq(ro_pulse) + else: + raise ValueError("measurement output is not 0 or 1") + + processed_values = IntegratedResults(values) + results[ro_pulse.qubit] = results[ro_pulse.serial] = processed_values return results diff --git a/tests/emulators/ibmfakebelem_q01/platform.py b/tests/emulators/ibmfakebelem_q01/platform.py index 136350a805..09a7b407b6 100644 --- a/tests/emulators/ibmfakebelem_q01/platform.py +++ b/tests/emulators/ibmfakebelem_q01/platform.py @@ -1,5 +1,7 @@ import pathlib +import numpy as np + from qibolab.channels import ChannelMap from qibolab.instruments.emulator.pulse_simulator import PulseSimulator from qibolab.platform import Platform @@ -25,6 +27,14 @@ def create(): instruments = {"pulse_simulator": pulse_simulator} instruments = load_instrument_settings(runcard, instruments) + # noise_model + SNR = 30 # dB + NOISE_AMP = np.power(10, -SNR / 20) + AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 + + if instruments["pulse_simulator"].readout_simulator_config is not None: + instruments["pulse_simulator"].readout_simulator_config["noise_model"] = AWGN + # extract quantities from runcard for platform declaration qubits, couplers, pairs = load_qubits(runcard) settings = load_settings(runcard) From b564e64a3bccabc938a6f471b6c1046640eb8a40 Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Wed, 9 Oct 2024 13:56:15 +0800 Subject: [PATCH 14/18] slight changes to account for LO_frequency in device configuration --- src/qibolab/instruments/emulator/readout.py | 22 ++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/src/qibolab/instruments/emulator/readout.py b/src/qibolab/instruments/emulator/readout.py index 9ba2e11dc3..8a96d034b6 100644 --- a/src/qibolab/instruments/emulator/readout.py +++ b/src/qibolab/instruments/emulator/readout.py @@ -54,7 +54,14 @@ class ReadoutSimulator: """Module for simulating a readout event based on simulated qubit state.""" def __init__( - self, qubit: Qubit, g, noise_model, internal_Q, coupling_Q, sampling_rate=1e9 + self, + qubit: Qubit, + g, + noise_model, + internal_Q, + coupling_Q, + sampling_rate=1e9, + **kwargs ): """Initalizer for the readout simulator. @@ -80,6 +87,7 @@ def __init__( + dispersive_shift(g, delta, qubit.anharmonicity) ) + self.lo_frequency = kwargs.get("LO_frequency", None) self.noise_model = noise_model self.sampling_rate = sampling_rate @@ -87,30 +95,30 @@ def __init__( self.ground_s21 = s21_function(ground_state_frequency, total_Q, coupling_Q) self.excited_s21 = s21_function(excited_state_frequency, total_Q, coupling_Q) - def simulate_ground_state_iq(self, pulse: ReadoutPulse, LO_frequency=None): + def simulate_ground_state_iq(self, pulse: ReadoutPulse): """Simulates the IQ result for a given readout pulse when the qubit is in the ground state. Args: pulse (qibolab.pulses.ReadoutPulse): Qibolab readout pulse. """ - if LO_frequency: - intermediate_frequency = np.abs(pulse.frequency - LO_frequency) + if self.lo_frequency is not None: + intermediate_frequency = np.abs(pulse.frequency - self.lo_frequency) else: intermediate_frequency = pulse.frequency s21 = self.ground_s21(intermediate_frequency) return self.simulate_and_demodulate(s21, pulse, intermediate_frequency) - def simulate_excited_state_iq(self, pulse: ReadoutPulse, LO_frequency=None): + def simulate_excited_state_iq(self, pulse: ReadoutPulse): """Simulates the IQ result for a given readout pulse when the qubit is in the excited state. Args: pulse (qibolab.pulses.ReadoutPulse): Qibolab readout pulse. """ - if LO_frequency: - intermediate_frequency = np.abs(pulse.frequency - LO_frequency) + if self.lo_frequency is not None: + intermediate_frequency = np.abs(pulse.frequency - self.lo_frequency) else: intermediate_frequency = pulse.frequency From 97faa4073743740eb2bdc3a0c379d018f8d8f33e Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Mon, 11 Nov 2024 16:45:38 +0800 Subject: [PATCH 15/18] handles multiple resonators, improves noise model implement, allows variable noise model --- .../instruments/emulator/pulse_simulator.py | 8 +++++- src/qibolab/instruments/emulator/readout.py | 27 ++++++++++++------- .../ibmfakebelem_q01/parameters.json | 20 ++++++++++---- tests/emulators/ibmfakebelem_q01/platform.py | 10 ------- 4 files changed, 39 insertions(+), 26 deletions(-) diff --git a/src/qibolab/instruments/emulator/pulse_simulator.py b/src/qibolab/instruments/emulator/pulse_simulator.py index e07794e239..90f2af51ea 100644 --- a/src/qibolab/instruments/emulator/pulse_simulator.py +++ b/src/qibolab/instruments/emulator/pulse_simulator.py @@ -206,7 +206,13 @@ def play( # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency, # such that the V_I/V_Q are maximally seperated (i.e.: bare_resonator_frequency = lambshifted readout pulse frequency + lamb shift) # solve quadratic equation for bare_resonator frequency - readout = ReadoutSimulator(qubit=qubit, **self.readout_simulator_config) + sampling_rate = self.readout_simulator_config["sampling_rate"] + readout_parameters = self.readout_simulator_config[ + str(get_qubit(ro_pulse.qubit, qubits)) + ] + readout = ReadoutSimulator( + qubit=qubit, sampling_rate=sampling_rate, **readout_parameters + ) values = np.array(samples[ro_pulse.qubit]).astype(np.complex128) for i in range(len(values)): if values[i] == 0: diff --git a/src/qibolab/instruments/emulator/readout.py b/src/qibolab/instruments/emulator/readout.py index 8a96d034b6..2cdbbf587a 100644 --- a/src/qibolab/instruments/emulator/readout.py +++ b/src/qibolab/instruments/emulator/readout.py @@ -54,24 +54,20 @@ class ReadoutSimulator: """Module for simulating a readout event based on simulated qubit state.""" def __init__( - self, - qubit: Qubit, - g, - noise_model, - internal_Q, - coupling_Q, - sampling_rate=1e9, - **kwargs + self, qubit: Qubit, g, internal_Q, coupling_Q, sampling_rate=1e9, **kwargs ): """Initalizer for the readout simulator. Args: qubit (qibolab.qubits.Qubit): Qibolab qubit object. g (float): Coupling strength between readout resonator and qubit in Hz. - noise_model (function): Time (in)dependent function that models the signal noise. internal_Q (float): Internal Q factor of the readout resonator. coupling_Q (float): Coupling/external Q factor of the readout resonator. sampling_rate (float): Sampling rate of the ADC/digitizer. + + kwargs: + noise_model (function): Time (in)dependent function that models the signal noise. + SNR: signal-to-noise ratio """ # maintaining the definition of |0> = |e> = (1 0) with the JC Hamiltonian model # ground_state_frequency = dressed resonator frequency when qubit is in ground state (vice versa for excited_state_frequency) @@ -87,8 +83,13 @@ def __init__( + dispersive_shift(g, delta, qubit.anharmonicity) ) + self.noise_model = kwargs.get("noise_model", "AWGN") + # if noise_model was not user-defined via readout.py, AWGN will be used by default + if self.noise_model.upper() == "AWGN": + self.noise_model = self.AWGN + + self.SNR = kwargs.get("SNR", 30) self.lo_frequency = kwargs.get("LO_frequency", None) - self.noise_model = noise_model self.sampling_rate = sampling_rate total_Q = 1 / (1 / internal_Q + 1 / coupling_Q) @@ -187,3 +188,9 @@ def simulate_and_demodulate( z = np.sum(z) / len(t) return z + + def AWGN(self, t): # noise model + SNR = self.SNR + NOISE_AMP = NOISE_AMP = np.power(10, -SNR / 20) + noise = np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 + return noise diff --git a/tests/emulators/ibmfakebelem_q01/parameters.json b/tests/emulators/ibmfakebelem_q01/parameters.json index 8e4289773b..ae4b783b7d 100644 --- a/tests/emulators/ibmfakebelem_q01/parameters.json +++ b/tests/emulators/ibmfakebelem_q01/parameters.json @@ -68,11 +68,21 @@ "instructions": 1 }, "readout_simulator_config": { - "g": 10e6, - "noise_model": 0, - "internal_Q": 2.5e6, - "coupling_Q": 6e4, - "sampling_rate": 1966.08e6 + "sampling_rate": 1966.08e6, + "0":{ + "g": 10e6, + "noise_model": "AWGN", + "SNR": 30 , + "internal_Q": 2.5e6, + "coupling_Q": 6e4 + }, + "1":{ + "g": 10e6, + "noise_model": "AWGN", + "SNR": 30 , + "internal_Q": 2.5e6, + "coupling_Q": 6e4 + } } } }, diff --git a/tests/emulators/ibmfakebelem_q01/platform.py b/tests/emulators/ibmfakebelem_q01/platform.py index 09a7b407b6..136350a805 100644 --- a/tests/emulators/ibmfakebelem_q01/platform.py +++ b/tests/emulators/ibmfakebelem_q01/platform.py @@ -1,7 +1,5 @@ import pathlib -import numpy as np - from qibolab.channels import ChannelMap from qibolab.instruments.emulator.pulse_simulator import PulseSimulator from qibolab.platform import Platform @@ -27,14 +25,6 @@ def create(): instruments = {"pulse_simulator": pulse_simulator} instruments = load_instrument_settings(runcard, instruments) - # noise_model - SNR = 30 # dB - NOISE_AMP = np.power(10, -SNR / 20) - AWGN = lambda t: np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 - - if instruments["pulse_simulator"].readout_simulator_config is not None: - instruments["pulse_simulator"].readout_simulator_config["noise_model"] = AWGN - # extract quantities from runcard for platform declaration qubits, couplers, pairs = load_qubits(runcard) settings = load_settings(runcard) From 03e3f9fc1f340eeea99841f9331f190f0b7dce25 Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Wed, 13 Nov 2024 15:39:44 +0800 Subject: [PATCH 16/18] calibrated readout parameters --- src/qibolab/instruments/emulator/readout.py | 4 ++-- tests/emulators/ibmfakebelem_q01/parameters.json | 12 ++++++------ 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/qibolab/instruments/emulator/readout.py b/src/qibolab/instruments/emulator/readout.py index 2cdbbf587a..c85c15fc71 100644 --- a/src/qibolab/instruments/emulator/readout.py +++ b/src/qibolab/instruments/emulator/readout.py @@ -84,7 +84,7 @@ def __init__( ) self.noise_model = kwargs.get("noise_model", "AWGN") - # if noise_model was not user-defined via readout.py, AWGN will be used by default + # if noise_model is not user-defined via readout.py, AWGN will be used by default if self.noise_model.upper() == "AWGN": self.noise_model = self.AWGN @@ -192,5 +192,5 @@ def simulate_and_demodulate( def AWGN(self, t): # noise model SNR = self.SNR NOISE_AMP = NOISE_AMP = np.power(10, -SNR / 20) - noise = np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 3e4 + noise = np.random.normal(loc=0, scale=NOISE_AMP, size=len(t)) * 4e3 return noise diff --git a/tests/emulators/ibmfakebelem_q01/parameters.json b/tests/emulators/ibmfakebelem_q01/parameters.json index ae4b783b7d..bfa01b6512 100644 --- a/tests/emulators/ibmfakebelem_q01/parameters.json +++ b/tests/emulators/ibmfakebelem_q01/parameters.json @@ -70,14 +70,14 @@ "readout_simulator_config": { "sampling_rate": 1966.08e6, "0":{ - "g": 10e6, + "g": 24e6, "noise_model": "AWGN", "SNR": 30 , "internal_Q": 2.5e6, "coupling_Q": 6e4 }, "1":{ - "g": 10e6, + "g": 20e6, "noise_model": "AWGN", "SNR": 30 , "internal_Q": 2.5e6, @@ -106,7 +106,7 @@ "MZ": { "duration": 22400, "amplitude": 0.03, - "frequency": 7301661824.000001, + "frequency": 7301877067.453677, "shape": "Rectangular()", "type": "ro", "start": 0, @@ -126,7 +126,7 @@ "MZ": { "duration": 22400, "amplitude": 0.056500000000000015, - "frequency": 7393428047.0, + "frequency": 7393567707.200529, "shape": "Rectangular()", "type": "ro", "start": 0, @@ -270,7 +270,7 @@ "single_qubit": { "0": { "bare_resonator_frequency": 7301616604.7871, - "readout_frequency": 7301661824.000001, + "readout_frequency": 7301877067.453677, "drive_frequency": 5090167234.445013, "anharmonicity": -336123005.1821652, "Ec": 0, @@ -286,7 +286,7 @@ }, "1": { "bare_resonator_frequency": 7393381494, - "readout_frequency": 7393428047.0, + "readout_frequency": 7393567707.200529, "drive_frequency": 5245306068.285917, "anharmonicity": -316572131.412737, "Ec": 0, From c632b67c4b9aa48d22fa03cece587b551a19477a Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Sun, 17 Nov 2024 21:56:19 +0800 Subject: [PATCH 17/18] implementation accounting for sweepers and averaging --- .../instruments/emulator/pulse_simulator.py | 165 +++++++++--------- 1 file changed, 83 insertions(+), 82 deletions(-) diff --git a/src/qibolab/instruments/emulator/pulse_simulator.py b/src/qibolab/instruments/emulator/pulse_simulator.py index 90f2af51ea..106770f93c 100644 --- a/src/qibolab/instruments/emulator/pulse_simulator.py +++ b/src/qibolab/instruments/emulator/pulse_simulator.py @@ -170,6 +170,7 @@ def play( dict: A dictionary mapping the readout pulses serial and respective qubits to Qibolab results object, as well as simulation-related time and states data. """ + self.qubits = qubits nshots = execution_parameters.nshots ro_pulse_list = sequence.ro_pulses times_dict, output_states, ro_reduced_dm, rdm_qubit_list = ( @@ -188,7 +189,9 @@ def play( if self.readout_error is not None: samples = apply_readout_noise(samples, self.readout_error) # generate result object - results = get_results_from_samples(ro_pulse_list, samples, execution_parameters) + results = self.get_results_from_samples( + ro_pulse_list, samples, execution_parameters + ) results["simulation"] = { "sequence_duration": times_dict["sequence_duration"], "simulation_dt": times_dict["simulation_dt"], @@ -196,35 +199,6 @@ def play( "output_states": output_states, } - if ( - execution_parameters.acquisition_type is AcquisitionType.INTEGRATION - and self.readout_simulator_config is not None - ): - for ro_pulse in ro_pulse_list: - # obtain qubit with its qubitID - qubit = qubits[get_qubit(ro_pulse.qubit, qubits)] - # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency, - # such that the V_I/V_Q are maximally seperated (i.e.: bare_resonator_frequency = lambshifted readout pulse frequency + lamb shift) - # solve quadratic equation for bare_resonator frequency - sampling_rate = self.readout_simulator_config["sampling_rate"] - readout_parameters = self.readout_simulator_config[ - str(get_qubit(ro_pulse.qubit, qubits)) - ] - readout = ReadoutSimulator( - qubit=qubit, sampling_rate=sampling_rate, **readout_parameters - ) - values = np.array(samples[ro_pulse.qubit]).astype(np.complex128) - for i in range(len(values)): - if values[i] == 0: - values[i] = readout.simulate_ground_state_iq(ro_pulse) - elif values[i] == 1: - values[i] = readout.simulate_excited_state_iq(ro_pulse) - else: - raise ValueError("measurement output is not 0 or 1") - - processed_values = IntegratedResults(values) - results[ro_pulse.qubit] = results[ro_pulse.serial] = processed_values - return results ### sweeper adapted from icarusqfpga ### @@ -257,6 +231,7 @@ def sweep( Raises: NotImplementedError: If sweep.parameter is not in AVAILABLE_SWEEP_PARAMETERS. """ + self.qubits = qubits sweeper_shape = [len(sweep.values) for sweep in sweeper] # Record pulse values before sweeper modification @@ -294,7 +269,7 @@ def sweep( simulation_time_array = np.array(simulation_time_array).reshape(sweeper_shape) # reshape and reformat samples to results format - results = get_results_from_samples( + results = self.get_results_from_samples( sequence.ro_pulses, sweep_samples, execution_parameters, sweeper_shape ) @@ -423,6 +398,83 @@ def _sweep_play( return times_dict, state_history, samples + def get_results_from_samples( + self, + ro_pulse_list: list, + samples: dict[Union[str, int], list], + execution_parameters: ExecutionParameters, + prepend_to_shape: list = [], + ) -> dict[str, Union[IntegratedResults, SampleResults]]: + """Converts samples into Qibolab results format. + + Args: + ro_pulse_list (list): List of readout pulse sequences. + samples (dict): Samples generated by get_samples. + append_to_shape (list): Specifies additional dimensions for the shape of the results. Defaults to empty list. + execution_parameters (`qibolab.ExecutionParameters`): Parameters (nshots, + relaxation_time, + fast_reset, + acquisition_type, + averaging_mode) + + Returns: + dict: Qibolab result data. + + Raises: + ValueError: If execution_parameters.acquisition_type is not supported. + """ + shape = prepend_to_shape + [execution_parameters.nshots] + tshape = [-1] + list(range(len(prepend_to_shape))) + + results = {} + for ro_pulse in ro_pulse_list: + values = np.array(samples[ro_pulse.qubit]).reshape(shape).transpose(tshape) + + if execution_parameters.acquisition_type is AcquisitionType.DISCRIMINATION: + processed_values = SampleResults(values) + + elif execution_parameters.acquisition_type is AcquisitionType.INTEGRATION: + processed_values = IntegratedResults(values.astype(np.complex128)) + if self.readout_simulator_config is not None: + # obtain qubit with its qubitID + qubit = self.qubits[get_qubit(ro_pulse.qubit, self.qubits)] + # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency, + # such that the V_I/V_Q are maximally seperated (i.e.: bare_resonator_frequency = lambshifted readout pulse frequency + lamb shift) + # solve quadratic equation for bare_resonator frequency + sampling_rate = self.readout_simulator_config["sampling_rate"] + readout_parameters = self.readout_simulator_config[ + str(get_qubit(ro_pulse.qubit, self.qubits)) + ] + readout = ReadoutSimulator( + qubit=qubit, sampling_rate=sampling_rate, **readout_parameters + ) + values = np.array(samples[ro_pulse.qubit]).astype(np.complex128) + for i in range(len(values)): + if values[i] == 0: + values[i] = readout.simulate_ground_state_iq(ro_pulse) + elif values[i] == 1: + values[i] = readout.simulate_excited_state_iq(ro_pulse) + else: + raise ValueError("Measurement output is not 0 or 1") + + values = ( + np.array(values).reshape(shape).transpose(tshape) + ) # converts samples into Qibolab results format + processed_values = IntegratedResults(values) + + else: + raise ValueError( + f"Current emulator does not support requested AcquisitionType {execution_parameters.acquisition_type}" + ) + + if execution_parameters.averaging_mode is AveragingMode.CYCLIC: + processed_values = ( + processed_values.average + ) # generates AveragedSampleResults + + results[ro_pulse.qubit] = results[ro_pulse.serial] = processed_values + return results + @staticmethod def merge_sweep_results( dict_a: """dict[str, Union[IntegratedResults, SampleResults, list]]""", @@ -700,57 +752,6 @@ def make_array_index_list(array_shape: list): return np.indices(array_shape).reshape(len(array_shape), -1).T -def get_results_from_samples( - ro_pulse_list: list, - samples: dict[Union[str, int], list], - execution_parameters: ExecutionParameters, - prepend_to_shape: list = [], -) -> dict[str, Union[IntegratedResults, SampleResults]]: - """Converts samples into Qibolab results format. - - Args: - ro_pulse_list (list): List of readout pulse sequences. - samples (dict): Samples generated by get_samples. - append_to_shape (list): Specifies additional dimensions for the shape of the results. Defaults to empty list. - execution_parameters (`qibolab.ExecutionParameters`): Parameters (nshots, - relaxation_time, - fast_reset, - acquisition_type, - averaging_mode) - - Returns: - dict: Qibolab result data. - - Raises: - ValueError: If execution_parameters.acquisition_type is not supported. - """ - shape = prepend_to_shape + [execution_parameters.nshots] - tshape = [-1] + list(range(len(prepend_to_shape))) - - results = {} - for ro_pulse in ro_pulse_list: - values = np.array(samples[ro_pulse.qubit]).reshape(shape).transpose(tshape) - - if execution_parameters.acquisition_type is AcquisitionType.DISCRIMINATION: - processed_values = SampleResults(values) - - elif execution_parameters.acquisition_type is AcquisitionType.INTEGRATION: - processed_values = IntegratedResults(values.astype(np.complex128)) - - else: - raise ValueError( - f"Current emulator does not support requested AcquisitionType {execution_parameters.acquisition_type}" - ) - - if execution_parameters.averaging_mode is AveragingMode.CYCLIC: - processed_values = ( - processed_values.average - ) # generates AveragedSampleResults - - results[ro_pulse.qubit] = results[ro_pulse.serial] = processed_values - return results - - def get_samples( nshots: int, ro_reduced_dm: np.ndarray, From 112587d9ac705a569ed205cd4f8ffb8231d3bf35 Mon Sep 17 00:00:00 2001 From: Chun Seng Date: Thu, 21 Nov 2024 16:11:09 +0800 Subject: [PATCH 18/18] changes accommodating readout for sweeping --- .../instruments/emulator/pulse_simulator.py | 39 ++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) diff --git a/src/qibolab/instruments/emulator/pulse_simulator.py b/src/qibolab/instruments/emulator/pulse_simulator.py index 106770f93c..677f52866c 100644 --- a/src/qibolab/instruments/emulator/pulse_simulator.py +++ b/src/qibolab/instruments/emulator/pulse_simulator.py @@ -89,6 +89,7 @@ def setup(self, **kwargs): self.output_state_history = simulation_config["output_state_history"] self.readout_simulator_config = kwargs.get("readout_simulator_config", None) + self.sweep_readout = False # no readout is carried out in sweeping by default """readout_simulator_config = Dict(g=, noise_model=, internal_Q=, coupling_Q=, sampling_rate=) Additional parameters needed for ReadoutSimulator for demodulation emulation. @@ -336,6 +337,8 @@ def _sweep_recursion( sweep = sweeper[0] param = sweep.parameter param_name = param.name.lower() + if param == Parameter.frequency and len(sweep.pulses.ro_pulses) != 0: + self.sweep_readout = True base_sweeper_values = [getattr(pulse, param_name) for pulse in sweep.pulses] sweeper_op = _sweeper_operation.get(sweep.type) @@ -396,6 +399,36 @@ def _sweep_play( if self.readout_error is not None: samples = apply_readout_noise(samples, self.readout_error) + #qubit readout for sweeping + if ( + execution_parameters.acquisition_type is AcquisitionType.INTEGRATION + and self.readout_simulator_config is not None + and self.sweep_readout == True + ): + for ro_pulse in ro_pulse_list: + # obtain qubit with its qubitID + qubit = qubits[get_qubit(ro_pulse.qubit, qubits)] + # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency, + # such that the V_I/V_Q are maximally seperated (i.e.: bare_resonator_frequency = lambshifted readout pulse frequency + lamb shift) + # solve quadratic equation for bare_resonator frequency + sampling_rate = self.readout_simulator_config["sampling_rate"] + readout_parameters = self.readout_simulator_config[ + str(get_qubit(ro_pulse.qubit, qubits)) + ] + readout = ReadoutSimulator( + qubit=qubit, sampling_rate=sampling_rate, **readout_parameters + ) + values = np.array(samples[ro_pulse.qubit]).astype(np.complex128) + for i in range(len(values)): + if values[i] == 0: + values[i] = readout.simulate_ground_state_iq(ro_pulse) + elif values[i] == 1: + values[i] = readout.simulate_excited_state_iq(ro_pulse) + else: + raise ValueError("measurement output is not 0 or 1") + + samples[ro_pulse.qubit] = list(values) + return times_dict, state_history, samples def get_results_from_samples( @@ -435,7 +468,11 @@ def get_results_from_samples( elif execution_parameters.acquisition_type is AcquisitionType.INTEGRATION: processed_values = IntegratedResults(values.astype(np.complex128)) - if self.readout_simulator_config is not None: + if ( + self.readout_simulator_config is not None + and self.sweep_readout == False + #qubit readout for sweeping is carried out individually after each sweep, due to variability of sweeping + ): # obtain qubit with its qubitID qubit = self.qubits[get_qubit(ro_pulse.qubit, self.qubits)] # bare_resonator_frequency is prepared considering readout pulse frequency = lambshifted readout pulse frequency,

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