arXiv Machine Learning July 14, 2026 By Arul Rhik Mazumder, Shreyan Ronit Mazumder

Depth-Efficient Quantum Topological Data Analysis for Regime-Specific Detection of Financial Stress

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arXiv:2607. 09906v1 Announce Type: cross Abstract: We present, to our knowledge, the first adaptation of Pauli Correlation Encoding (PCE) to quantum topological data analysis, reformulating Betti number estimation as a depth-efficient variational optimization over a compressed qubit register.

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arXiv Machine Learning

Jul 17

An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor

arXiv:2604. 23743v2 Announce Type: replace-cross Abstract: Variational quantum circuits train poorly on chaotic forecasting, usually blamed on barren plateaus (exponentially vanishing gradients).

By Tushar Pandey

arXiv Machine Learning

Jun 3

Spectral Anatomy of Quantum Gaussian Process Kernels

arXiv:2605. 30952v2 Announce Type: replace Abstract: Two recent results have reshaped quantum Gaussian processes (QGPs).

By Jian Xu, Chao Li, Guang Lin, Yuning Qiu, Delu Zeng, John Paisley, Qibin Zhao

arXiv Machine Learning

Aug 6

Dynamical Lie Algebras Cannot Describe Shallow QAOA: Cragged Terrains, Barren Plateaus, and Empirical Hardness Models

arXiv:2608. 04252v1 Announce Type: cross Abstract: The dynamical Lie algebraic (DLA) theory of variational quantum algorithms (VQAs) predicts commonplace exponentially vanishing loss and gradient variances for sufficiently deep parametrized circuits.

By Harrison Copp, Charlton Li, An\v{z}ej Margeta-Cacace, Amy Qiao

arXiv Machine Learning

Sep 15

Certification cost of quantum models: measurement correlation, not parameter count

arXiv:2609.14424v1 Announce Type: cross Abstract: Reporting the Fisher geometry of a trained variational quantum model is routine; quoting the shot budget that would establish it is not. Certifying a...

By Pavel Sulimov, Claude Lehmann

arXiv Machine Learning

Jun 24

Foundations of Practical Quantum Advantage in Quantum-Informed Machine Learning for Predicting Chaos

arXiv:2606. 13422v2 Announce Type: replace-cross Abstract: We develop theoretical foundations for a practical quantum-advantage mechanism in quantum-informed machine learning for chaotic dynamical systems.

By Maida Wang, Xiao Xue, Minh Chung, Peter V. Coveney

arXiv Machine Learning

Jul 3

Ravines in quantum cost landscapes: opportunities for improved VQA predictions

arXiv:2607. 01329v1 Announce Type: cross Abstract: The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantum algorithms (VQAs).

By Felix J. Beckmann, Jo\~ao F. Bravo

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