Merge branch 'Qiskit-Extensions:main' into Setting-type-for-composite…

…-experiment

qiskit_experiments/library/randomized_benchmarking/standard_rb.py

@@ -241,6 +241,9 @@ def _get_basis_gates(self) -> Optional[Tuple[str, ...]]:
             return tuple(sorted(basis_gates)) if basis_gates else None
 
         def is_bidirectional(coupling_map):
+            if coupling_map is None:
+                # None for a coupling map implies all-to-all coupling
+                return True
             return len(coupling_map.reduce(self.physical_qubits).get_edges()) == 2
 
         # 2 qubits case: Return all basis gates except for one-way directed 2q-gates.

releasenotes/notes/rb-v2-none-coupling-fda2b22afdef507b.yaml

@@ -0,0 +1,12 @@
+---
+fixes:
+  - |
+    Changed :class:`.StandardRB` to treat two qubit operations in the
+    :class:`qiskit.transpiler.Target` as having all-to-all connectivity if
+    there is no set of specific pairs of coupled qubits. Most importantly, this
+    change allows :class:`.StandardRB` to work with
+    :class:`qiskit_aer.AerSimulator` for multi-qubit benchmarking after
+    ``qiskit-aer`` 0.13.0. Version 0.13.0 of ``qiskit-aer`` changed
+    the default :class:`qiskit_aer.AerSimulator` to have such a
+    :class:`qiskit.transpiler.Target` without specific coupled pairs.
+    See `#1292 <https://github.com/Qiskit-Extensions/qiskit-experiments/issues/1292>`__.

requirements-extras.txt

@@ -1,5 +1,5 @@
 qiskit-ibm-provider>=0.6.1 # for submitting experiments to backends through the IBM provider
 cvxpy>=1.3.2 # for tomography
 scikit-learn # for discriminators
-qiskit-aer>=0.11.0,<=0.12.2 # Temporary version pin because of https://github.com/Qiskit-Extensions/qiskit-experiments/issues/1292
+qiskit-aer>=0.11.0
 qiskit_dynamics>=0.4.0 # for the PulseBackend

test/library/randomized_benchmarking/test_standard_rb.py

@@ -319,11 +319,29 @@ def test_three_qubit(self):
 
     def test_add_more_circuit_yields_lower_variance(self):
         """Test variance reduction with larger number of sampling."""
+
+        # Increase single qubit error so that we can see gate error with a
+        # small number of Cliffords since we want to run many samples without
+        # taking too long.
+        p1q = 0.15
+        pvz = 0.0
+
+        # setup noise model
+        sx_error = depolarizing_error(p1q, 1)
+        rz_error = depolarizing_error(pvz, 1)
+
+        noise_model = NoiseModel()
+        noise_model.add_all_qubit_quantum_error(sx_error, "sx")
+        noise_model.add_all_qubit_quantum_error(rz_error, "rz")
+
+        # Aer simulator
+        backend = AerSimulator(noise_model=noise_model, seed_simulator=123)
+
         exp1 = rb.StandardRB(
-            physical_qubits=(0, 1),
+            physical_qubits=(0,),
             lengths=list(range(1, 30, 3)),
             seed=123,
-            backend=self.backend,
+            backend=backend,
             num_samples=3,
         )
         exp1.analysis.set_options(gate_error_ratio=None)
@@ -332,11 +350,11 @@ def test_add_more_circuit_yields_lower_variance(self):
         self.assertExperimentDone(expdata1)
 
         exp2 = rb.StandardRB(
-            physical_qubits=(0, 1),
+            physical_qubits=(0,),
             lengths=list(range(1, 30, 3)),
             seed=456,
-            backend=self.backend,
-            num_samples=5,
+            backend=backend,
+            num_samples=30,
         )
         exp2.analysis.set_options(gate_error_ratio=None)
         exp2.set_transpile_options(**self.transpiler_options)

test/library/tomography/tomo_utils.py

@@ -87,7 +87,9 @@ def readout_noise_model(num_qubits, seed=None):
     p1g0s = 0.15 * rng.random(num_qubits)
     p0g1s = 0.3 * rng.random(num_qubits)
     amats = np.stack([[1 - p1g0s, p1g0s], [p0g1s, 1 - p0g1s]]).T
-    noise_model = NoiseModel()
+    # Set `basis_gates` so that reset is included.
+    # See https://github.com/Qiskit/qiskit-aer/issues/1975
+    noise_model = NoiseModel(basis_gates=["id", "rz", "sx", "cx", "reset"])
     for i, amat in enumerate(amats):
         noise_model.add_readout_error(amat.T, [i])
     return noise_model