Quantum moves
Introduction
Quantum Moves was an experiment in tackling heavy computational search problems by applying quantum-inspired optimization to game theory. Specifically, I built a quadratic unconstrained binary optimization (QUBO) based solver designed to tackle the classic chess mate-in-N problem. Chess move calculations involve massive branching decision trees, which makes them a great benchmark for testing how mathematical optimization models handle complex constraints.
Measuring
To see how well the QUBO approach actually performed, I ran performance comparisons against traditional engines like Stockfish, which relies on alpha-beta pruning and deep heuristic searches. I also benchmarked it against a custom hybrid model that combined AlphaZero reinforcement learning policies with quantum-inspired sampling methods. Building this required a deep dive into how mathematical constraints are translated into binary optimization formulas, offering a fascinating look at the current boundaries of quantum computing approaches when applied to deterministic strategy games.