Add a tool for measuring expectation values of Pauli strings with readout error mitigation #7067
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…of qubits as input
… readout error mitigation
…CalibrationResult. Besides, fixed some lints
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Hey @eliottrosenberg and @NoureldinYosri if you could take a look at this. Thanks! |
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Very nice work Danni!! This looks really great!
A few initial comments:
Regarding line 55 in shuffle_circuits_with_readout_benchmarking.py, it should be possible to specify 0 for num_random_bitstrings in order to do the measurement without readout mitigation (or have a different way of running without readout mitigation).
I think you went with lists of tuples for circuits_to_pauli because you can't put a cirq.Circuit into a dictionary, but you can put a cirq.FrozenCircuit into a dictionary, so maybe that would be more convenient (not sure). Also, the object that measure_pauli_strings outputs is pretty complicated, with lots of nested tuples, so maybe we want to create some data class for the output?
…_random_bitstrings, and add a test to cover the situation. The design is the run_shuffled_with_readout_benchmarking method will return a empty SingleQubitReadoutCalibrationResult if no calibration is actually done. 2. Allow measure_pauli_strings to take num_random_bitstrings = 0. In this case, no mitigation is actually done, and the mitigated result and unmitigated result are the same. Also add a test to handle this situation. 3. Make the return type of measure_pauli_strings a data class.
Done. |
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Thanks, Danni!! I tried testing it in this colab and got the error shown there.
Can you try again? The problem should be fixed in the latest commit ;) |
This PR does the following two things:
Improved Readout Benchmarking: Modified run_shuffled_with_readout_benchmarking to accept a list of qubit tuples (List[Tuple[Qubit]]). This allows for calculating the readout error rate on specific qubit subsets, ensuring alignment with the qubit sets used in non-identity Pauli operators within Pauli strings.
Added a tool to compute expectation values for Pauli operators. This tool calculates both unmitigated and readout-error-mitigated expectation values for each circuit, and integrates the results from the readout benchmarking.