Merge branch 'main' into numpy

docs/tutorials/calibrations.rst

@@ -196,8 +196,8 @@ Run the calibration experiment:
 
 The instance of ``calibrations`` has been automatically updated with the measured
 frequency, as shown below. In addition to the columns shown below, ``calibrations`` also
-store the group to which a value belongs, whether a values is valid or not and the
-experiment id that produce a value.
+stores the group to which a value belongs, whether a value is valid or not, and the
+experiment id that produced a value.
 
 .. jupyter-execute::
 

qiskit_experiments/calibration_management/__init__.py

@@ -23,7 +23,7 @@
     otherwise indicated.
 
 Calibrating qubit setups is the task of finding the pulse shapes and parameter
-values that maximizes the fidelity of the resulting quantum operations. This
+values that maximize the fidelity of the resulting quantum operations. This
 therefore requires experiments which are analyzed to extract parameter values.
 Furthermore, the resulting parameter values and schedules must be managed. The
 calibration management module in Qiskit experiments allows users to manage

qiskit_experiments/framework/experiment_data.py

@@ -1681,7 +1681,7 @@ def save(
 
         Args:
             suppress_errors: should the method catch exceptions (true) or
-            pass them on, potentially aborting the experiment (false)
+                pass them on, potentially aborting the experiment (false)
             max_workers: Maximum number of concurrent worker threads (capped by 10)
             save_figures: Whether to save figures in the database or not
             save_children: For composite experiments, whether to save children as well

qiskit_experiments/library/randomized_benchmarking/standard_rb.py

@@ -22,7 +22,7 @@
 from numpy.random import Generator, default_rng
 from numpy.random.bit_generator import BitGenerator, SeedSequence
 
-from qiskit.circuit import QuantumCircuit, Instruction, Barrier
+from qiskit.circuit import CircuitInstruction, QuantumCircuit, Instruction, Barrier
 from qiskit.exceptions import QiskitError
 from qiskit.providers import BackendV2Converter
 from qiskit.providers.backend import Backend, BackendV1, BackendV2
@@ -291,7 +291,7 @@ def _sequences_to_circuits(
             circ = QuantumCircuit(self.num_qubits)
             for elem in seq:
                 circ.append(self._to_instruction(elem, basis_gates), circ.qubits)
-                circ.append(Barrier(self.num_qubits), circ.qubits)
+                circ._append(CircuitInstruction(Barrier(self.num_qubits), circ.qubits))
 
             # Compute inverse, compute only the difference from the previous shorter sequence
             prev_elem = self.__compose_clifford_seq(prev_elem, seq[len(prev_seq) :])

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 until 0.13.1 release
+qiskit-aer>=0.11.0
 qiskit_dynamics>=0.4.0 # for the PulseBackend

requirements.txt

@@ -1,6 +1,6 @@
 numpy>=1.17
 scipy>=1.4
-qiskit>=0.44,<1.0
+qiskit>=0.44
 qiskit-ibm-experiment>=0.3.4
 matplotlib>=3.4
 uncertainties

test/library/tomography/tomo_utils.py

@@ -87,9 +87,7 @@ 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
-    # 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"])
+    noise_model = NoiseModel()
     for i, amat in enumerate(amats):
         noise_model.add_readout_error(amat.T, [i])
     return noise_model