Active Learning for Quantum Mechanical Measurements

TL;DR

This paper proposes an active learning approach to optimize the allocation of experimental repetitions in quantum measurements, reducing the resources needed for accurate estimation as system size grows.

Contribution

It introduces an active learning scheme that improves measurement efficiency in quantum experiments, especially for large systems.

Findings

Active learning reduces experimental repetitions needed for quantum measurements.

Efficiency gains increase with the size of the quantum system.

The method enhances the precision of observable estimations.

Abstract

The experimental evaluation of many quantum mechanical quantities requires the estimation of several directly measurable observables, such as local observables. Due to the necessity to repeat experiments on individual quantum systems in order to estimate expectation values of observables, the question arises how many repetitions to allocate to a given directly measurable observable. We show that an active learning scheme can help to improve such allocations, and the resultant decrease in experimental repetitions required to evaluate a quantity with the desired accuracy increases with the size of the underlying quantum mechanical system.