Reinforcement learning for Quantum Tiq-Taq-Toe
On the map: Deep reinforcement learning methods → Quantum reinforcement learning
Abstract (arXiv)
Quantum Tiq-Taq-Toe is a well-known benchmark and playground for both quantum computing and machine learning. Despite its popularity, no reinforcement learning (RL) methods have been applied to Quantum Tiq-Taq-Toe. Although there has been some research on Quantum Chess this game is significantly more complex in terms of computation and analysis. Therefore, we study the combination of quantum computing and reinforcement learning in Quantum Tiq-Taq-Toe, which may serve as an accessible testbed for the integration of both fields. Quantum games are challenging to represent classically due to their inherent partial observability and the potential for exponential state complexity. In Quantum Tiq-Taq-Toe, states are observed through Measurement (a 3x3 matrix of state probabilities) and Move History (a 9x9 matrix of entanglement relations), making strategy complex as each move can collapse the quantum state.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 15 references that are in the map. First posted on arXiv 10 Nov 2024.
Builds on
4 of 15 references.
References · 4
- 2017Attention Is All You Needcited by 60,361
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
- 2018Optimizing Quantum Error Correction Codes with Reinforcement Learningcited by 29
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
- 2026Reinforcement Learning for Quantum Technology
- 2025Benchmarking Quantum Reinforcement Learning
- 2025Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Components
- 2025Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
- 2026Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
- 2026Quantum entanglement provides a competitive advantage in adversarial games
- 2024A quantum-classical reinforcement learning model to play Atari games