AI in large search spaces with a domain agnostic approach

Abstract

This dissertation presents a systematic comparison of classical and quantum machine learning for solving the 3×3×3 Rubik’sCube, a benchmark for high-dimensional discrete search spaces. The classical approach uses an optimised QUBE reinforcement learning architecture with structured state representations, distance-based reward shaping, and Bayesian hyperparameter optimisation. The quantum approach employs a 6-qubit Variational Quantum Circuit (VQC) with amplitude encoding trained end-to-end on full-cube states. A hybrid quantum classical architecture is also evaluated. Results show the classical system achieves 65% System Success Rate (SSR) with stable solution trajectories. The quantum VQC demonstrates functional learning and extreme parameter compactness but achieves only 12% SSR due to circuit-depth limitations, barren plateau effects, and computational costs. The hybrid architecture achieves 15% SSR. The findings provide rigorous comparison under identical evaluation conditions, highlighting optimised classical RL’s strong performance, NISQ era quantum constraints, and hybrid architectures’ potential as hardware and algorithms mature.