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.
Nitrogen-vacancy (NV) centers in diamond can serve as highly sensitive solid-state quantum sensors for high-sensitivity magnetometry. However, in the noisy intermediate-scale quantum (NISQ) era, extra...
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:2308. 11290v2 Announce Type: replace-cross Abstract: Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality.
arXiv:2608. 13521v1 Announce Type: cross Abstract: Quantum technology has the potential to transform scientific discovery, but quantum advantages often require processing capabilities well beyond the reach of experimental platforms.
arXiv:2606. 15983v1 Announce Type: cross Abstract: Recent theoretical progress has established conditions under which machine learning models can efficiently predict ground-state properties of gapped local Hamiltonians when trained on quantum-generated data.
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
arXiv:2608. 01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues.
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:2412. 09486v2 Announce Type: replace-cross Abstract: The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other.
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov chain methods.
arXiv:2507. 18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.