Merge branch 'master' into clifford_simulator_numba

doc/source/api-reference/qibo.rst

@@ -1677,6 +1677,14 @@ Frame Potential
 .. autofunction:: qibo.quantum_info.frame_potential
 
 
+Quantum Networks
+^^^^^^^^^^^^^^^^
+
+.. autoclass:: qibo.quantum_info.quantum_networks.QuantumNetwork
+    :members:
+    :member-order: bysource
+
+
 Random Ensembles
 ^^^^^^^^^^^^^^^^
 
@@ -2073,6 +2081,12 @@ Shannon entropy
 .. autofunction:: qibo.quantum_info.shannon_entropy
 
 
+Total Variation distance
+""""""""""""""""""""""""
+
+.. autofunction:: qibo.quantum_info.total_variation_distance
+
+
 Hellinger distance
 """"""""""""""""""
 

src/qibo/backends/clifford.py

@@ -117,6 +117,14 @@ def apply_gate_clifford(self, gate, symplectic_matrix, nqubits):
 
         return operation(symplectic_matrix, *gate.init_args, nqubits, **kwargs)
 
+    def apply_channel(self, channel, state, nqubits):
+        probabilities = channel.coefficients + (1 - np.sum(channel.coefficients),)
+        index = np.random.choice(range(len(probabilities)), size=1, p=probabilities)[0]
+        if index != len(channel.gates):
+            gate = channel.gates[index]
+            state = gate.apply_clifford(self, state, nqubits)
+        return state
+
     def execute_circuit(self, circuit, initial_state=None, nshots: int = 1000):
         """Execute a Clifford circuits.
 
@@ -131,7 +139,11 @@ def execute_circuit(self, circuit, initial_state=None, nshots: int = 1000):
             (:class:`qibo.quantum_info.clifford.Clifford`): Object giving access to the final results.
         """
         for gate in circuit.queue:
-            if not gate.clifford and not gate.__class__.__name__ == "M":
+            if (
+                not gate.clifford
+                and not gate.__class__.__name__ == "M"
+                and not isinstance(gate, gates.PauliNoiseChannel)
+            ):
                 raise_error(RuntimeError, "Circuit contains non-Clifford gates.")
 
         if circuit.repeated_execution and not nshots == 1:

src/qibo/gates/abstract.py

@@ -358,7 +358,7 @@ def matrix(self, backend=None):
             ndarray: Matrix representation of gate.
 
         .. note::
-            ``Gate.matrix`` was an defined as an atribute in ``qibo`` versions prior to  ``0.2.0``.
+            ``Gate.matrix`` was defined as an atribute in ``qibo`` versions prior to  ``0.2.0``.
             From ``0.2.0`` on, it has been converted into a method and has replaced the ``asmatrix`` method.
         """
         if backend is None:  # pragma: no cover

src/qibo/gates/channels.py

@@ -40,6 +40,9 @@ def apply(self, backend, state, nqubits):  # pragma: no cover
     def apply_density_matrix(self, backend, state, nqubits):
         return backend.apply_channel_density_matrix(self, state, nqubits)
 
+    def apply_clifford(self, backend, state, nqubits):
+        return backend.apply_channel(self, state, nqubits)
+
     def to_choi(self, nqubits: Optional[int] = None, order: str = "row", backend=None):
         """Returns the Choi representation :math:`\\mathcal{E}`
         of the Kraus channel :math:`\\{K_{\\alpha}\\}_{\\alpha}`.

src/qibo/gates/special.py

@@ -157,3 +157,8 @@ def fuse(self, gate):
                 if neighbor is not None:
                     parent.left_neighbors[q] = neighbor
                     neighbor.right_neighbors[q] = parent
+
+    def apply_clifford(self, backend, state, nqubits):
+        for gate in self.gates:
+            state = gate.apply_clifford(backend, state, nqubits)
+        return state

src/qibo/models/error_mitigation.py

@@ -314,7 +314,8 @@ def CDR(
 
     train_val = {"noise-free": [], "noisy": []}
     for circ in training_circuits:
-        val = circ(nshots=nshots).expectation_from_samples(observable)
+        result = backend.execute_circuit(circ, nshots=nshots)
+        val = result.expectation_from_samples(observable)
         train_val["noise-free"].append(val)
         val = get_expectation_val_with_readout_mitigation(
             circ, observable, noise_model, nshots, readout, qubit_map, backend=backend
@@ -400,7 +401,8 @@ def vnCDR(
     train_val = {"noise-free": [], "noisy": []}
 
     for circ in training_circuits:
-        val = circ(nshots=nshots).expectation_from_samples(observable)
+        result = backend.execute_circuit(circ, nshots=nshots)
+        val = result.expectation_from_samples(observable)
         train_val["noise-free"].append(val)
         for level in noise_levels:
             noisy_c = get_noisy_circuit(circ, level, insertion_gate=insertion_gate)
@@ -897,7 +899,7 @@ def ICS(
     lambda_list = []
 
     for training_circuit in training_circuits:
-        circuit_result = training_circuit(nshots=nshots)
+        circuit_result = backend.execute_circuit(training_circuit, nshots=nshots)
         expectation = observable.expectation_from_samples(circuit_result.frequencies())
 
         noisy_expectation = get_expectation_val_with_readout_mitigation(

src/qibo/quantum_info/__init__.py

@@ -1,6 +1,7 @@
 from qibo.quantum_info.basis import *
 from qibo.quantum_info.clifford import *
 from qibo.quantum_info.metrics import *
+from qibo.quantum_info.quantum_networks import *
 from qibo.quantum_info.random_ensembles import *
 from qibo.quantum_info.superoperator_transformations import *
 from qibo.quantum_info.utils import *