Sequential Bayesian experiment design for adaptive Ramsey sequence measurements

TL;DR

This paper introduces a sequential Bayesian experiment design protocol to optimize phase accumulation time in Ramsey measurements, significantly improving efficiency over heuristic and random methods in quantum phase estimation.

Contribution

The paper develops and demonstrates a Bayesian experiment design method for Ramsey measurements, reducing measurement time and computational overhead compared to existing strategies.

Findings

Bayesian design is twice as fast as heuristic protocol.

Bayesian design is four times faster than random choices.

Workflow integration minimizes measurement overhead.

Abstract

The Ramsey sequence is a canonical example of a quantum phase measurement for a spin qubit. In Ramsey measurements, the measurement efficiency can be optimized through careful selection of settings for the phase accumulation time setting, $\tau$. This paper implements a sequential Bayesian experiment design protocol for the phase accumulation time in low-fidelity Ramsey measurements, and performance is compared to both a previously reported adaptive heuristic protocol and random setting choices. A workflow allowing measurements and design calculations to run concurrently largely eliminates computation time from measurement overhead. When precession frequency is the lone parameter to estimate, the Bayesian design is faster by factors of 2 and 4 relative to the adaptive heuristic and random protocols respectively.