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.
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arXiv:2607. 22516v1 Announce Type: cross Abstract: A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data.
arXiv:2606. 31536v1 Announce Type: new Abstract: As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory.
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.
The paper develops a finite‑measurement framework for inferring the symmetry group that a quantum learning model should respect, based on candidate transformations and limited data. It shows that observable‑invisible transformations correspond to the stabilizer of a projected state when the probe span is invariant, and that recovered generators form a valid subgroup with a continuous invisible space identified via its Lie algebra. The authors introduce an unbiased shadow statistic that improves estimation rates, establish optimal gap dependence through a commuting‑qubit lower bound, and provide tools for task validation, bias quantification, and capacity analysis, all illustrated with Ising‑chain calculations.
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
The paper introduces a quantum method for extracting spectral features from the density of states (DOS) of a problem-dependent Hamiltonian, applied to signed graphs represented as Ising models. Using standardized moments of the Ising DOS as features, the authors demonstrate that these moments count signed closed walks, are switching‑invariant, and size‑free. On a benchmark of 140,000 labeled graphs, the exact DOS predicts the frustration index exactly, while five moments achieve a mean error of 0.4, and a new DOS‑QPE protocol offers efficient sampling with far fewer shots than classical trace‑sampling methods.