Adaptive Bayesian Single-Shot Quantum Sensing
This work addresses the challenge of efficient quantum sensing for applications like precision measurements, though it appears incremental as it builds on existing variational quantum sensing with a Bayesian adaptation.
The paper tackles the problem of optimizing quantum sensing probes and measurements, which is classically intractable due to high-dimensional Hilbert spaces, by introducing an adaptive Bayesian protocol that maximizes active information gain, resulting in a method tailored for non-asymptotic regimes and extended to support multiple sensing agents.
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.