SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918485861335040 |
|---|---|
| author | Ludmir, Jason DiBrita, Nicholas S. Han, Jason Patel, Tirthak |
| author_facet | Ludmir, Jason DiBrita, Nicholas S. Han, Jason Patel, Tirthak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04416 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility Ludmir, Jason DiBrita, Nicholas S. Han, Jason Patel, Tirthak Quantum Physics Emerging Technologies 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. |
| title | SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2605.04416 |