Reinforcement learning for Quantum Tiq-Taq-Toe
This work addresses the lack of RL methods for Quantum Tiq-Taq-Toe, serving as an accessible testbed for quantum computing and ML integration, though it appears incremental as it extends existing ideas to a new game.
The paper tackled the problem of applying reinforcement learning to Quantum Tiq-Taq-Toe, a benchmark for quantum computing and machine learning, by studying the integration of these fields to address challenges like partial observability and exponential state complexity, but no concrete results or numbers were reported.
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.