The paper develops a theoretical framework for quantum extreme learning machines (QELMs) using the Pauli transfer matrix (PTM) formalism. It shows how encoding, reservoir dynamics, and measurement observables jointly determine the nonlinear information processing capacity of a QELM, and provides interpretable diagnostics that can guide model design. The authors apply their analysis to forecasting nonlinear dynamical systems, demonstrating that a trained QELM’s readout closely reproduces the underlying flow map at leading orders.
By Markus Gross, Hans-Martin Rieser
arXiv:2606. 28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled with a finite number of circuit executions.
By Jian Xu, Delu Zeng, Qibin Zhao
arXiv:2606. 08276v1 Announce Type: cross Abstract: Quantum reinforcement learning (QRL) is a promising approach to learn effective decision strategies across several applications with stochastic environments.
By Alexander DeRieux, Walid Saad
arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.
By Alona Sakhnenko, Christian B. Mendl, Jeanette M. Lorenz
arXiv:2607. 01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel.
By Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa Jose
The paper investigates whether quantum reinforcement learning algorithms can be matched by efficient classical methods. It focuses on a simplified reinforcement learning setting with a uniform generative model, providing finite‑sample guarantees for classical kernelized Fitted Q‑Iteration that uses kernels aligned with parameterized quantum circuits. The authors identify sufficient conditions on data encoding, kernel choice, and problem structure under which this classical approach dequantizes quantum Q‑learning, and suggest using kernelized Fitted Q‑Iteration as a heuristic when those conditions cannot be verified.
By Pablo Rodriguez-Grasa, Sofiene Jerbi, Mikel Sanz, Ryan Sweke
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:2606. 19947v1 Announce Type: cross Abstract: Reliable quantum control in the presence of decoherence requires policies that combat the effect of environmental noise on the controlled dynamics.
By Merijn Moody, Zier Mensch, Miranda C. N. Cheng, Peter G. Bolhuis, Max Welling
arXiv:2308. 11290v2 Announce Type: replace-cross Abstract: Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality.
By Yuxuan Du, Yibo Yang, Tongliang Liu, Zhouchen Lin, Bernard Ghanem, Dacheng Tao
The paper introduces task‑resolved Fisher spectroscopy for quantum reservoir computing, defining orthonormal score coordinates from prediction targets that reweight labeled histories to produce an affine family of reservoir states and measurement outcomes. It establishes a Fisher‑information hierarchy linking state quantum Fisher information, measurement record Fisher information, and moment matrices up to many‑body order, providing a quadratic form that equals the stationary capacity of the optimal linear readout. The method requires only stationary labeled records and measured outcomes, enabling predictions of held‑out capacities, necessary feature order, and measurement‑budget dependence, and demonstrates how optimizing local measurement axes can recover hidden task information in a five‑spin open reservoir.
By Yang Peng
arXiv:2606. 11814v1 Announce Type: cross Abstract: Machine-learning approaches to quantum state tomography can achieve high reconstruction fidelity, but the physical structure used by the trained model often remains implicit.
By Xinge Wu, Huaxin Wang, Jiajun Liu, Ruiqing He, Jiandong Shang, Hengliang Guo, Qiang Chen
arXiv:2609.00372v1 Announce Type: cross
Abstract: With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rathe...
By Robin Lorenz, Eric Brunner, Marcello Benedetti