一句话：提出 AccelerQ 框架，将量子本征求解器实现视为黑盒，结合机器学习与遗传算法自动调优超参数以提升精度与效率。
量子本征求解器（QE）实现的超参数调优对精度与效率至关重要，但手工调整困难，且大规模系统难以经典模拟。AccelerQ 将 QE 实现视为黑盒程序，用机器学习与搜索优化自动调参：先以不超过 16 量子比特的小系统数据训练程序特定的轻量 XGBoost 回归器，再结合遗传算法探索超参数空间并避免局部最优，依据分子局部物理相互作用跨尺度持续的假设泛化到更大系统。在 ADAPT-QSCI 与 QCELS 上评估，模型用于 20/24/28 量子比特哈密顿量。ADAPT-QSCI 误差从 5.48% 降至 5.3%，加遗传算法到 5.05%；QCELS 从 7.5% 降至 6.5%，遗传算法无额外增益。部分 20/24 量子比特结果不确定，但结果显示了 ML 与优化在量子程序中的潜力。
We present AccelerQ , a framework for automatically tuning quantum eigensolver (QE) implementations– these are quantum programs implementing a specific QE algorithm–using machine learning and searchbased optimisation. Rather than redesigning quantum algorithms or manually tweaking the code of an already existing implementation, AccelerQ treats QE implementations as black-box programs and learns to optimise their hyperparameters to improve accuracy and efficiency by incorporating search-based techniques and genetic algorithms (GA) alongside ML models to efficiently explore the hyperparameter space of QE implementations and avoid local minima. Our approach leverages two ideas: 1) train on data from smaller, classically simulable systems, and 2) use program-specific ML models, exploiting the fact that local physical interactions in molecular systems persist across scales, supporting generalisation to larger systems. We present an empirical evaluation of AccelerQ on two fundamentally different QE implementations: ADAPT-QSCI and QCELS. For each, we trained a QE predictor model, a lightweight XGBoost Python regressor, using data extracted classically from systems of up to 16 qubits. We deployed the model to optimise hyperparameters for executions on larger systems of 20-, 24-, and 28-qubit Hamiltonians, where direct classical simulation becomes impractical. We observed a reduction in error from 5.48% to 5.3% with only the ML model and further to 5.05% with GA for ADAPT-QSCI, and from 7.5% to 6.5%, with no additional gain with GA for QCELS. Given inconclusive results for some 20- and 24-qubit systems, we recommend further analysis of training data concerning Hamiltonian characteristics. Nonetheless, our results highlight the potential of ML and optimisation techniques for quantum programs and suggest promising directions for integrating software engineering methods into quantum software stacks.
@article{paperbot3025,
title = {AccelerQ: Accelerating Quantum Eigensolvers with Machine Learning on Quantum Simulators},
author = {Avner Bensoussan and Elena Chachkarova and Karine Even-Mendoza and Sophie Fortz and Connor Lenihan},
journal = {Proceedings of the ACM on Programming Languages},
volume = {9},
number = {OOPSLA2},
year = {2025},
doi = {10.1145/3763132}
}