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Use primitives for solving the relaxed problem #52
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The new QAOA implementation in qiskit-terra assumes the problem Hamiltonian is diagonal so that it can benefit from certain additional features, like applying CVaR or other aggregation functions. However, for us the problem Hamiltonian is not diagonal. So, we implement QAOA using the `QAOAAnsatz` together with `VQE`.
Parts of this follow qiskit-community/qiskit-optimization#436
(and suppress the `PendingDeprecationWarning`s using pytest)
This way, it will be more clear when this branch/block can be removed in the future
garrison
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needs: changelog
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Summary
In this PR, I update the solving of the relaxed problem to use the new primitives-based interfaces in Qiskit Terra 0.22 and higher (
minimum_eigensolvers, rather than the oldminimum_eigen_solvers). I did not update the tutorials because I think it is better to update the tutorials in one pass once magic rounding has also been migrated to use the primitives (#21). All changes are backwards compatible: the code works with the oldeigen_solversas well. I kept the tests using the old solvers because there is one code path specific to theLegacyNumPyMinimumEigensolver.Details and comments
Estimatorprimitive is able to handle aPauliSumOpbut not aPauliOpdirectly, so I had to modify the return type ofqubit_op(no longer aUnion, yay!) andterm2op.test_backends.pyand update the solvers used once Incorporate qiskit primitives into magic rounding #21 is closed.QAOAinminimum_eigensolversintetionally assumes the problem Hamiltonian is diagonal so it can offer some additional features. However, this assumption does not hold for QRAO encoded Hamiltonians. In this case, QAOA is equivalent toVQEwith theQAOAAnsatz, so that is how we perform QAOA with QRAO using theEstimatorprimitive.