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QML for Quantum Sensing under Measurement-Induced Information Loss
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arXiv AI
Aug 26
QML for Quantum Sensing under Measurement-Induced Information Loss
The paper investigates quantum machine learning (QML) for magnetic-field estimation using nitrogen‑vacancy (NV) centers in diamond. By framing sensing as a supervised regression task, the authors compare classical machine learning models trained on measurement‑based data with quantum kernel‑based models trained on pre‑measurement coherent quantum states. They find that QML performance improves markedly when coherent quantum‑state information is available, whereas changes in model complexity or learning paradigm have little effect, highlighting the need for tightly integrated quantum‑sensor and QML pipelines.
arXiv Machine Learning
Sep 11
Bayesian quantum sensing using graybox machine learning
The paper reports the first experimental implementation of a graybox modelling strategy for a solid-state open quantum system. By combining a physics-based system model with a data-driven description of experimental imperfections, the graybox approach achieves higher fidelity than purely analytical models while requiring fewer training resources than fully deep-learning blackbox models. Using roughly 10,000 training datapoints, the graybox model improves mean squared error by several orders of magnitude over the physics-only model and outperforms a comparable blackbox model in estimating a static magnetic field with a single-spin quantum sensor.
arXiv Machine Learning
Jul 13
Is data-efficient learning feasible with quantum models?
arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.
arXiv Machine Learning
Jul 2
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
arXiv:2607. 01197v1 Announce Type: new Abstract: Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches.
arXiv AI
Jul 2
When AI meets quantum information: A comprehensive review
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
arXiv Machine Learning
Jul 7
Canonical quantization of neurons
arXiv:2607. 05000v1 Announce Type: cross Abstract: Canonical quantization provides a systematic procedure for constructing quantum models from classical Hamiltonians.