SMU Data Science Review

Molecular Quantum Particle Algorithm (MQPA): Hybrid Quantum-Classical Learning for Molecular Property Prediction

Jessica T. McPhaul, Southern Methodist UniversityFollow
Bivin Sadler Ph.D., Southern Methodist UniversityFollow

Abstract

Classical machine learning models and quantum kernel methods often struggle to capture quantum-coherent molecular features under the constraints of noisy intermediate-scale quantum (NISQ) hardware, limiting both predictive accuracy and scalability.

This paper introduces the Molecular Quantum Particle Algorithm (MQPA), a hybrid quantum–classical framework designed to achieve chemically accurate property prediction by integrating handcrafted molecular descriptors with parameterized quantum circuits. Molecular inputs, expressed as SMILES strings, are processed via RDKit and encoded through angle-based quantum gates with entangling layers in Qiskit [1]. Quantum parameters are optimized using simultaneous perturbation stochastic approximation (SPSA) [2], while classical regression layers leverage Adam [3] with Low-Rank Adaptation (LoRA) fine-tuning [4].

Across 38 systematically varied subsets of the QM9 benchmark [5,6], MQPA achieves a mean absolute error (MAE) of 0.03 kcal/mol, outperforming both deep learning and quantum kernel baselines by 41% and surpassing the chemical accuracy threshold of 0.1 kcal/mol [7–9]. Extensive cross-platform benchmarking—including IBM Quantum, Qiskit Aer, and NVIDIA H100 clusters—demonstrates strong reproducibility, with inter-backend MAE variance below 3%.

Positioned as a scalable and hardware-aware solution, MQPA offers practical advantages for quantum-enhanced molecular property prediction across applications in quantum chemistry, drug discovery, materials design, and environmental modeling.

Recommended Citation

McPhaul, Jessica T. and Sadler, Bivin Ph.D. (2025) "Molecular Quantum Particle Algorithm (MQPA): Hybrid Quantum-Classical Learning for Molecular Property Prediction," SMU Data Science Review: Vol. 9: No. 1, Article 1.
Available at: https://scholar.smu.edu/datasciencereview/vol9/iss1/1