arXiv Machine Learning
Guiding Agents of Quantum Games to Equilibrium using Matrix Exponential Fixed-Point Iteration
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arXiv AI
Sep 10
Riemannian Optimization for Multi-Player Quantum Games on Product Unitary Manifolds
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.
arXiv AI
Sep 11
Reinforcement learning for Quantum Tiq-Taq-Toe
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.
arXiv Machine Learning
2d ago
Average-and Last-Iterate Lower Bounds for Optimistic Matrix Mirror-Prox in Quantum Zero-Sum Games
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...
arXiv AI
Aug 5
Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
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.
arXiv Machine Learning
Jul 2
Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization
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.
arXiv Machine Learning
Jul 13
Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning
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.