SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility
For researchers building hybrid quantum-classical systems, SpinTune offers a practical software solution to mitigate environmental decoherence, a key bottleneck limiting quantum sensor reliability.
SpinTune uses reinforcement learning to autonomously discover adaptive dynamical decoupling sequences that improve coherence preservation in quantum sensors, outperforming standard methods in a simulated Carbon-13 spin bath.
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.