linear_inv_process_estimate

forest.benchmarking.tomography.linear_inv_process_estimate(results: List[forest.benchmarking.observable_estimation.ExperimentResult], qubits: List[int]) → numpy.ndarray

Estimate a quantum process using linear inversion.

This is the simplest process tomography post processing. To use this function, collect process tomography data with generate_process_tomography_experiment() and estimate_observables().

For more details on this post-processing technique, see https://en.wikipedia.org/wiki/Quantum_tomography#Linear_inversion or see section 3.5 of [WOOD]

Parameters:

• results – A tomographically complete list of results.
• qubits – All qubits that were tomographized. This specifies the order in which qubits will be kron’ed together; the first qubit in the list is the left-most tensor factor.

Returns:

A point estimate of the quantum process represented by a Choi matrix