arXiv:2606. 18713v1 Announce Type: new Abstract: Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement.
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learning remains largely unexplored.
arXiv:2605. 22097v2 Announce Type: replace-cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints.
arXiv:2511. 12482v2 Announce Type: replace-cross Abstract: Quantum error correction is essential for fault-tolerant quantum computing.
arXiv:2608. 00850v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a versatile approach for solving nonlinear partial differential equations (PDEs), yet achieving high accuracy efficiently using these techniques remains challenging for high-dimensional or multiscale systems.
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:2607. 20045v1 Announce Type: cross Abstract: Physical noise in near-term quantum hardware is usually treated as a nuisance to suppress.
arXiv:2512. 01317v3 Announce Type: replace-cross Abstract: Measurement-induced entanglement (MIE) captures how local measurements generate long-range quantum correlations and drive dynamical phase transitions in many-body systems.
arXiv:2604. 15645v2 Announce Type: replace Abstract: We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid quantum-classical architectures within a unified modular workflow.
arXiv:2606. 09756v1 Announce Type: new Abstract: Responses to perturbations are key to understanding physical systems.
arXiv:2607. 21688v1 Announce Type: cross Abstract: Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box.
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
arXiv:2607. 00063v1 Announce Type: cross Abstract: This paper studies how spectral geometry emerges in quantum learning models and how it can be diagnosed with physically grounded probes.