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

TL;DR

This paper introduces an online control method for variational quantum sensing that uses conformal inference to provide reliable, real-time error bounds, improving the robustness of quantum sensors on NISQ devices.

Contribution

It presents a novel online control framework integrating conformal inference with variational quantum sensing, offering guaranteed error bounds and improved reliability.

Findings

Maintains reliable estimates over time in quantum magnetometry

Provides deterministic error bars with guaranteed risk levels

Enhances practical quantum sensing on noisy intermediate-scale devices

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.