Merge branch 'master' into entangling_capacity

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
       - id: check-yaml
       - id: debug-statements
   - repo: https://github.com/psf/black
-    rev: 23.12.0
+    rev: 23.12.1
     hooks:
       - id: black
   - repo: https://github.com/pycqa/isort

doc/source/code-examples/advancedexamples.rst

@@ -801,13 +801,11 @@ Using density matrices
 Qibo circuits can evolve density matrices if they are initialized using the
 ``density_matrix=True`` flag, for example:
 
-.. testsetup::
+.. testcode::
 
     import qibo
     qibo.set_backend("qibojit")
 
-.. testcode::
-
     from qibo import models, gates
 
     # Define circuit

pyproject.toml

@@ -63,7 +63,7 @@ pytest-cov = "^4.0.0"
 pylint = "^3.0.3"
 matplotlib = "^3.7.0"
 qibojit = { git = "https://github.com/qiboteam/qibojit.git" }
-tensorflow = { version = "^2.15.0", markers = "sys_platform == 'linux'" }
+tensorflow = { version = "^2.14.1", markers = "sys_platform == 'linux'" }
 
 [tool.poe.tasks]
 test = "pytest"

src/qibo/backends/numpy.py

@@ -638,7 +638,7 @@ def samples_to_binary(self, samples, nqubits):
     def samples_to_decimal(self, samples, nqubits):
         qrange = self.np.arange(nqubits - 1, -1, -1, dtype="int32")
         qrange = (2**qrange)[:, self.np.newaxis]
-        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)

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)

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)

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)