Efficient Bayesian Phase Estimation

TL;DR

This paper introduces a new adaptive Bayesian phase estimation algorithm that directly infers phase and uncertainty, offering improved flexibility, robustness, and speed over existing methods, even under experimental imperfections.

Contribution

The paper presents a novel adaptive Bayesian algorithm for phase estimation that bypasses bit-by-bit inference, enhancing efficiency and robustness in noisy quantum experiments.

Findings

Algorithm is highly flexible and recovers from failures.

Performs well under substantial decoherence and imperfections.

Comparable or faster than existing phase estimation methods.

Abstract

We provide a new efficient adaptive algorithm for performing phase estimation that does not require that the user infer the bits of the eigenphase in reverse order; rather it directly infers the phase and estimates the uncertainty in the phase directly from experimental data. Our method is highly flexible, recovers from failures, and can be run in the presence of substantial decoherence and other experimental imperfections and is as fast or faster than existing algorithms.