SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility

TL;DR

SpinTune is a reinforcement learning-based method that autonomously discovers adaptive dynamical decoupling sequences to enhance the reliability of quantum sensors in noisy environments.

Contribution

It introduces a novel reinforcement learning approach for optimizing dynamical decoupling sequences tailored to specific environmental noise.

Findings

SpinTune outperforms standard DD sequences in simulations of a Carbon-13 spin bath.

It significantly improves coherence preservation in quantum sensors.

The method adapts to realistic noise conditions for better reliability.

Abstract

Emerging quantum sensors are increasingly envisioned as components of hybrid quantum-classical high-performance computing, enabling new capabilities in scientific, cyber-physical, and machine-learning pipelines. However, their practical utility is limited by environmental decoherence, which degrades sensing reliability. While dynamical decoupling (DD) pulse sequences can mitigate this, standard methods are often suboptimal in the presence of realistic noise. We present SpinTune, a reinforcement learning software approach that autonomously discovers adaptive, piecewise DD sequences tailored to specific environments. Using a simulation model of a Carbon-13 spin bath, we show that SpinTune significantly outperforms standard DD sequences in preserving coherence.