Observable Estimation and Error Mitigation

The module observable_estimation is at the heart of forest benchmarking. It provides a convenient way to construct experiments that measure observables and mitigate errors associated with readout (measurement) process.

• Observable Estimation
  • Create the experiment
  • Run the experiment
  • Error mitigation
    • Symmetrizing readout
    • Calibrating estimates of symmetrized observable expectations
  • Estimate compatible observables from the same data
  • Easy Parallelization
  • More on ExperimentSetting’s in_state and observable
  • Breaking estimate_observables into parts
    • Construct an ObservablesExperiment and turn it into a list of Programs
    • At this point we can run directly or first symmetrize
      • 1) run programs directly
      • 2) First symmetrize, then run
    • Now we can translate our bitarray shots into ExperimentResults with expectation values

Data structures

ObservablesExperiment(settings, …)	A data structure for experiments involving estimation of the expectation of various observables measured on a core program, possibly with a collection of different preparations.
ExperimentSetting(in_state, observable)	Input and output settings for an ObservablesExperiment.
ExperimentResult(setting, expectation, …)	An expectation and standard deviation for the measurement of one experiment setting in an ObservablesExperiment.
TensorProductState([states])	A description of a multi-qubit quantum state that is a tensor product of many _OneQStates states.
_OneQState(label, index, qubit)	A description of a named one-qubit quantum state.
to_json(fn, obj)	Convenience method to save forest.benchmarking.observable_estimation objects as a JSON file.
read_json(fn)	Convenience method to read forest.benchmarking.observable_estimation objects from a JSON file.

Functions

estimate_observables(qc, obs_expt, …)	Standard wrapper for estimating the observables in an ObservableExperiment.
calibrate_observable_estimates(qc, …)	Calibrates the expectation and std_err of the input expt_results and updates those estimates.
generate_experiment_programs(obs_expt, …)	Generate the programs necessary to estimate the observables in an ObservablesExperiment.
group_settings(obs_expt, method)	Group settings that are diagonal in a shared tensor product basis (TPB) to minimize number of QPU runs.
shots_to_obs_moments(bitarray, qubits, …)	Calculate the mean and variance of the given observable based on the bitarray of results.
ratio_variance(a, numpy.ndarray], var_a, …)	Given random variables ‘A’ and ‘B’, compute the variance on the ratio Y = A/B.
merge_disjoint_experiments(experiments, …)	Merges the list of experiments into a single experiment that runs the sum of the individual experiment programs and contains all of the combined experiment settings.
get_results_by_qubit_groups(results, …)	Organizes ExperimentResults by the group of qubits on which the observable of the result acts.