The Study of Auto-playing Algorithm for Quantum
Session Number
PHYS 10
Advisor(s)
Dr. Kishor T. Kapale, Western Illinois University, Department of Physics
Discipline
Physical Science
Start Date
17-4-2024 10:25 AM
End Date
17-4-2024 10:40 AM
Abstract
A popular Artificial Intelligence (AI) algorithm in the gaming world used for the Robo player is called Minimax. This paper presents research aimed at improving the efficiency of minimax algorithms in the context of quantum chess, a variant of traditional chess that incorporates principles of quantum mechanics. Quantum chess introduces additional complexity due to the superposition and entanglement of quantum pieces, leading to a vastly larger game tree compared to classical chess. The traditional minimax method becomes very slow when the depth of an internally simulated gameplay increases. Moreover, the minimax can find a wrong move as it may miss a costly move by the opponent lurking beyond the depth being searched. This is called a Horizon effect. We took an approach called “quiescence search” and applied it to quantum chess. It looks beyond the simulated depth till the end of the game to identify the lurking danger and eliminate those moves from the search space. We compared the quiescence algorithm with the minimax with move ordering. Our simulation results demonstrate the effectiveness of the proposed techniques for quantum chess.
The Study of Auto-playing Algorithm for Quantum
A popular Artificial Intelligence (AI) algorithm in the gaming world used for the Robo player is called Minimax. This paper presents research aimed at improving the efficiency of minimax algorithms in the context of quantum chess, a variant of traditional chess that incorporates principles of quantum mechanics. Quantum chess introduces additional complexity due to the superposition and entanglement of quantum pieces, leading to a vastly larger game tree compared to classical chess. The traditional minimax method becomes very slow when the depth of an internally simulated gameplay increases. Moreover, the minimax can find a wrong move as it may miss a costly move by the opponent lurking beyond the depth being searched. This is called a Horizon effect. We took an approach called “quiescence search” and applied it to quantum chess. It looks beyond the simulated depth till the end of the game to identify the lurking danger and eliminate those moves from the search space. We compared the quiescence algorithm with the minimax with move ordering. Our simulation results demonstrate the effectiveness of the proposed techniques for quantum chess.