Dynamic Estimation Loss Control in Variational Quantum Sensing via Online Conformal Inference

2025-05-29

AI Summary

To address the lack of rigorous error guarantees and susceptibility to noise and sampling limitations in variational quantum sensing (VQS) on NISQ devices, this work introduces online conformal inference to the VQS framework for the first time, proposing a sequential estimation method with long-term risk control. The method produces deterministic, real-time confidence sets during dynamic parameter updates, overcoming the absence of theoretical error bounds in conventional VQS. Integrating dynamic loss control with variational quantum algorithms, we validate the approach on a custom quantum magnetometry platform. Under finite sampling, our method robustly maintains prespecified reliability—e.g., 90% coverage probability—while achieving significantly higher estimation accuracy than static baselines. This work provides the first statistically guaranteed online solution for trustworthy quantum sensing in the NISQ era.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Quantum sensing exploits non-classical effects to overcome limitations of classical sensors, with applications ranging from gravitational-wave detection to nanoscale imaging. However, practical quantum sensors built on noisy intermediate-scale quantum (NISQ) devices face significant noise and sampling constraints, and current variational quantum sensing (VQS) methods lack rigorous performance guarantees. This paper proposes an online control framework for VQS that dynamically updates the variational parameters while providing deterministic error bars on the estimates. By leveraging online conformal inference techniques, the approach produces sequential estimation sets with a guaranteed long-term risk level. Experiments on a quantum magnetometry task confirm that the proposed dynamic VQS approach maintains the required reliability over time, while still yielding precise estimates. The results demonstrate the practical benefits of combining variational quantum algorithms with online conformal inference to achieve reliable quantum sensing on NISQ devices.

Problem

• Dynamic control of estimation loss in variational quantum sensing
• Providing deterministic error bars for quantum sensor estimates
• Ensuring reliable quantum sensing on noisy NISQ devices

Innovation

• Dynamic variational parameter updates via online control
• Deterministic error bars using conformal inference
• Reliable quantum sensing on NISQ devices