Adaptive Bayesian Single-Shot Quantum Sensing

2025-07-22

AI Summary

Quantum sensing in high-dimensional Hilbert spaces faces significant challenges in jointly optimizing quantum probes and measurement strategies, particularly in single-shot, non-asymptotic regimes where classical computation becomes intractable. Method: We propose an adaptive Bayesian variational framework: (i) parameterized quantum circuits serve as programmable probes, integrating quantum channel evolution with classical feedback; (ii) active information gain maximization guides control policy design; and (iii) Bayesian online posterior updating enables real-time adaptation. The framework supports multi-agent collaborative estimation and fusion. Contribution/Results: Compared to conventional frequentist offline optimization, our method achieves substantially improved parameter estimation accuracy and faster convergence in single-shot scenarios. Empirical validation confirms that multi-agent collaboration yields measurable information gain advantages, demonstrating scalability and robustness in high-dimensional quantum sensing tasks.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation
• Machine Learning: Quantum Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Large-scale security measurements

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.

Problem

• Optimizing quantum sensing probes in high-dimension Hilbert spaces
• Adaptive Bayesian inference for single-shot quantum sensing
• Fusing estimates from multiple quantum sensing agents

Innovation

• Adaptive Bayesian optimization for quantum sensing
• Single-shot variational quantum sensing protocol
• Multi-agent quantum sensing fusion technique