Quantum Chess AI
Session Number
CMPS 35
Advisor(s)
Dr. Kishor Kapale, Western Illinois University
Discipline
Computer Science
Start Date
17-4-2025 11:25 AM
End Date
17-4-2025 11:40 AM
Abstract
Research into AI models and game theory involving extensive form games has been applied to economic models, computer science (Ikeda, K, 2023), and current reinforcement learning models such as AlphaZero. There is potential for advances in AI understanding quantum principles to have the same effect on quantum computing and technology. Quantum Chess is one of the first “quantum extensive form games” which involve the phenomena of superposition, entanglement, and phase (Cantwell, C, 2019). In this article we explore the application of reinforcement learning to the value and policy systems of Monte Carlo Tree Search. We then evaluate the basic understanding of quantum principles by having the AI complete chess puzzles requiring a quantum move solution and recording both the time taken and win rate.
Quantum Chess AI
Research into AI models and game theory involving extensive form games has been applied to economic models, computer science (Ikeda, K, 2023), and current reinforcement learning models such as AlphaZero. There is potential for advances in AI understanding quantum principles to have the same effect on quantum computing and technology. Quantum Chess is one of the first “quantum extensive form games” which involve the phenomena of superposition, entanglement, and phase (Cantwell, C, 2019). In this article we explore the application of reinforcement learning to the value and policy systems of Monte Carlo Tree Search. We then evaluate the basic understanding of quantum principles by having the AI complete chess puzzles requiring a quantum move solution and recording both the time taken and win rate.