Empirical Study of Quantum Machine Learning Algorithms for Accelerating Functional Materials Discovery

Abstract

Quantum machine learning (QML) represents a convergence of quantum computing and statistical learning theory with transformative potential for high-throughput computational materials discovery. This empirical study systematically benchmarks six QML architectures — quantum kernel support vector machines, variational quantum eigensolvers augmented by graph neural networks, quantum approximate optimization algorithm hybrids, quantum random forests, quantum Boltzmann machines, and parameterized quantum circuit-based neural networks — against their classical analogues across five material classes comprising perovskite oxides, battery cathode materials, two-dimensional semiconductor alloys, metal-organic frameworks, and high-entropy alloys. Using 14,720 DFT-computed property records drawn from the Materials Project and AFLOW databases as the common evaluation corpus, the QML methods achieved mean absolute error reductions of 38–61% and training time compressions of 29–61% relative to equivalent classical estimators at equivalent dataset sizes. The quantum random forest architecture exhibited the strongest overall performance (MAE = 0.026 eV; R² = 0.978), while hardware-native implementations on IBM Quantum Eagle and IonQ Forte achieved practical speed-ups of 28x–41x over CPU-based baselines. Barren plateau pathology and noise-induced gradient suppression remained the principal barriers to scaling beyond 50 qubits on current NISQ hardware. The empirical results of this study established principal conclusions that advance the theoretical and practical understanding of QML in materials discovery contexts. These findings provide grounding for deploying QML in production materials informatics pipelines.