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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

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

arXiv Machine Learning

Jun 19

Effective Dimension Governs Generalization in Quantum Kernel Vision Models

arXiv:2606. 20183v1 Announce Type: new Abstract: Recent quantum vision models-quantum vision transformers and quantum convolutional networks-report two striking but unexplained empirical phenomena: (i) ansatze with more, or more uniformly distributed, entanglement generalize better, and (ii) injecting quantum noise can improve test accuracy rather than degrade it.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

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

Spectral Anatomy of Quantum Gaussian Process Kernels

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

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