The paper extends the Eisert-Wilkens-Lewenstein quantum game to multiplayer settings with mixed strategies, where each player selects unitary operators and mixes them classically. It introduces the Unitary Strategy Matrix Exponential Algorithm (USMEA), a geometry-aware sequential method that jointly learns local unitary actions and mixing probabilities for each player. The authors analyze USMEA’s convergence under standard conditions and confirm the theory with numerical experiments, demonstrating how classical optimization can be integrated into engineered quantum strategic interactions.
By Alireza Habibi, Setareh Maghsudi
Quantum Tiq‑Taq‑Toe is a popular benchmark for quantum computing and machine learning, yet no reinforcement learning (RL) methods have been applied to it. The paper introduces RL techniques for this game, which is simpler than Quantum Chess but still challenging due to partial observability and exponential state complexity. States are represented by a 3×3 measurement matrix and a 9×9 move‑history matrix of entanglement relations, making strategy development difficult because each move can collapse the quantum state.
By Catalin-Viorel Dinu, Thomas Moerland
arXiv:2609.38835v1 Announce Type: cross
Abstract: Optimistic matrix mirror-prox (OMMP) computes $\epsilon$-approximate Nash equilibria in quantum zero-sum games with an $O(1/\varepsilon)$ average-ite...
By Yiheng Su, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Pucheng Xiong
arXiv:2608. 02826v1 Announce Type: cross Abstract: Reinforcement learning is a subfield of machine learning that studies how an agent interacts with an environment in order to extract as large a reward as possible.
By Joao F. Doriguello
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
arXiv:2607. 09422v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays.
By Edwin De Nicolo, Rahul Marchand, Cornelius Carlsson, Pranav Vaidhyanathan, Natalia Ares
arXiv:2608. 14319v1 Announce Type: new Abstract: We study quantum multi-armed bandits (QMAB) and quantum linear bandits (QLB) in the model of Wan et al.
By Maoli Liu, Zhuohua Li, John C. S. Lui
arXiv:2607. 20225v1 Announce Type: cross Abstract: While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces.
By Seongmin Kim, Abhinav Rijal, Yuri Alexeev, Nora Bauer, Martin Roetteler, Mina Yoon, George Siopsis, In-Saeng Suh
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. 06480v1 Announce Type: cross Abstract: Many real-world competitive systems require multiple decision-makers to act simultaneously under shared constraints, limited information, and repeated interaction, as in auctions, resource allocation, and security competition.
By Qintong Xie, Edward Koh, Xavier Cadet, Peter Chin
arXiv:2609.39164v1 Announce Type: new
Abstract: Score-based variational inference (VI) provides an alternative to Kullback--Leibler (KL)-based VI by minimizing the Fisher divergence between the varia...
By Yuchen Cong, Zerui Tao, Chao Li, Zhe Sun, Qibin Zhao
arXiv:2603. 10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question.
By Peiyong Wang, Kieran Hymas, James Quach