PR #94 introduces benchmarking code that allows our ZNE methods to be tested against randomly generated n-qubit quantum circuits.
We have also discussed wanting to benchmark our performance on a variational algorithm.
Proposal
This issue proposes to add a new module maxcut_py to /benchmarks that runs MAXCUT problems on random graphs and evaluates how the mitigated result compares to the unmitigated result.
Background code
It is likely easiest to do all of our base benchmarking in cirq, as is done in the random circuits example. This means we don't have to worry about circuit conversion and can take advantage of the convenient noise simulation built into cirq.
Cirq does not have a well maintained QAOA/MAXCUT implementation, but they do have an example here. The example isn't great and perhaps we could maybe upstream an update to it when we do our implementation.
Wanted to see if you all thought this was a good idea before moving forward on it.
PR #94 introduces benchmarking code that allows our ZNE methods to be tested against randomly generated n-qubit quantum circuits.
We have also discussed wanting to benchmark our performance on a variational algorithm.
Proposal
This issue proposes to add a new module
maxcut_py
to/benchmarks
that runs MAXCUT problems on random graphs and evaluates how the mitigated result compares to the unmitigated result.Background code
It is likely easiest to do all of our base benchmarking in cirq, as is done in the random circuits example. This means we don't have to worry about circuit conversion and can take advantage of the convenient noise simulation built into cirq.
Cirq does not have a well maintained QAOA/MAXCUT implementation, but they do have an example here. The example isn't great and perhaps we could maybe upstream an update to it when we do our implementation.
Wanted to see if you all thought this was a good idea before moving forward on it.
@andreamari @nathanshammah @rmlarose @tudorgt @yhindy