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- return self.np.matmul(samples, qrange)[:, 0] + return self.np.matmul(self.to_numpy(samples), qrange)[:, 0] def calculate_frequencies(self, samples): res, counts = self.np.unique(samples, return_counts=True) diff --git a/src/qibo/quantum_info/metrics.py b/src/qibo/quantum_info/metrics.py index dd162462d3..239935e006 100644 --- a/src/qibo/quantum_info/metrics.py +++ b/src/qibo/quantum_info/metrics.py @@ -878,7 +878,7 @@ def gate_error(channel, target=None, check_unitary: bool = False, backend=None): return error -def diamond_norm(channel, target=None, **kwargs): +def diamond_norm(channel, target=None, backend=None, **kwargs): """Calculates the diamond norm :math:`\\|\\mathcal{E}\\|_{\\diamond}` of ``channel`` :math:`\\mathcal{E}`, which is given by @@ -938,7 +938,8 @@ def diamond_norm(channel, target=None, **kwargs): # `CVXPY` only works with `numpy`, so this function has to # convert any channel to the `numpy` backend by default - backend = GlobalBackend() + if backend is None: # pragma: no cover + backend = GlobalBackend() channel = backend.to_numpy(channel) channel = np.transpose(channel) diff --git a/tests/test_hamiltonians.py b/tests/test_hamiltonians.py index 19a22437d8..ff495d19a2 100644 --- a/tests/test_hamiltonians.py +++ b/tests/test_hamiltonians.py @@ -183,7 +183,7 @@ def test_hamiltonian_matmul_states(backend, sparse_type): else: if backend.name == "tensorflow": pytest.skip("Tensorflow does not support operations with sparse matrices.") - nqubits = 5 + nqubits = 3 nstates = 2**nqubits matrix = random_sparse_matrix(backend, nstates, sparse_type) H = hamiltonians.Hamiltonian(nqubits, matrix, backend=backend) diff --git a/tests/test_quantum_info_metrics.py b/tests/test_quantum_info_metrics.py index 2903102839..cb42305fcd 100644 --- a/tests/test_quantum_info_metrics.py +++ b/tests/test_quantum_info_metrics.py @@ -565,10 +565,10 @@ def test_diamond_norm(backend, nqubits): unitary = backend.identity_density_matrix(nqubits, normalize=False) unitary = to_choi(unitary, order="row", backend=backend) - dnorm = diamond_norm(unitary) + dnorm = diamond_norm(unitary, backend=backend) backend.assert_allclose(dnorm, 1.0, atol=PRECISION_TOL) - dnorm = diamond_norm(unitary, unitary) + dnorm = diamond_norm(unitary, unitary, backend=backend) backend.assert_allclose(dnorm, 0.0, atol=PRECISION_TOL)