Adaptive Bayesian Single-Shot Quantum Sensing

TL;DR

This paper introduces an adaptive Bayesian protocol for quantum sensing that optimizes measurement strategies in real-time, improving precision in single-shot scenarios and allowing multiple sensors to work together.

Contribution

It presents a novel adaptive Bayesian approach for variational quantum sensing, enhancing single-shot measurement efficiency and multi-agent data fusion.

Findings

Effective optimization of sensing policies via active information gain.

Improved precision in non-asymptotic, single-probe regimes.

Extension to multi-agent quantum sensing systems.

Abstract

Quantum sensing harnesses the unique properties of quantum systems to enable precision measurements of physical quantities such as time, magnetic and electric fields, acceleration, and gravitational gradients well beyond the limits of classical sensors. However, identifying suitable sensing probes and measurement schemes can be a classically intractable task, as it requires optimizing over Hilbert spaces of high dimension. In variational quantum sensing, a probe quantum system is generated via a parameterized quantum circuit (PQC), exposed to an unknown physical parameter through a quantum channel, and measured to collect classical data. PQCs and measurements are typically optimized using offline strategies based on frequentist learning criteria. This paper introduces an adaptive protocol that uses Bayesian inference to optimize the sensing policy via the maximization of the active information gain. The proposed variational methodology is tailored for non-asymptotic regimes where a single probe can be deployed in each time step, and is extended to support the fusion of estimates from multiple quantum sensing agents.