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Remove workarounds for qiskit-aer 0.13.0 #1330

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Nov 29, 2023
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Remove workarounds for qiskit-aer 0.13.0 #1330

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4533402
Remove workarounds for qiskit-aer 0.13.0
wshanks Nov 28, 2023
29959e1
Merge branch 'main' into undo1294
wshanks Nov 28, 2023
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2 changes: 1 addition & 1 deletion requirements-extras.txt
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Original file line number Diff line number Diff line change
@@ -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
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Should this be

Suggested change
qiskit-aer>=0.11.0
qiskit-aer>=0.11.0, !=0.13.0

to be safe? Or should we just raise the requirement to 0.13.1?

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I put that at first but then took it out. The issues we were avoiding in 0.13.0 were Aer bugs (not treating reset as a default basis gate, changing the default simulation method leading to slower performance) that affected the tests. They were not actually issues with the experiments code. So I think the better argument would be to put !=0.13.0 into constraints.txt but in practice it's probably not worth the effort since anyone updating Aer now should end up with 0.13.1 any way, so avoiding 0.13.0 is a pretty niche case. It wouldn't hurt much though (just maybe the extra overhead of someone in the future seeing that in constraints.txt and thinking about it before taking it out).

qiskit_dynamics>=0.4.0 # for the PulseBackend
4 changes: 1 addition & 3 deletions test/library/tomography/tomo_utils.py
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Expand Up @@ -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