SchNet的变分量子电路参数化：保守分子力场的基于模拟器的可行性研究
Variational Quantum Circuit Parameterization of SchNet: A Simulator-Based Feasibility Study for Conservative Molecular Force Fields
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中文总结 AI 辅助
本研究提出混合量子SchNet架构，将变分量子电路模块集成到SchNet框架中，在8个MD17分子系统上验证，能量-力联合监督大幅提升预测精度，证明该架构可端到端训练以改善分子能量和力预测。
中文摘要 AI 辅助
机器学习力场通过用可微的分子能量和原子力模型替代昂贵的量子化学计算，为加速分子模拟提供了有前景的途径。然而，学习准确且能量守恒的力仍然具有挑战性，尤其是当模型必须从有限数据中同时捕捉全局能量趋势和局部势能梯度时。在本研究中，我们提出了一种混合量子SchNet架构，该架构将变分量子电路模块集成到连续滤波SchNet框架中。量子模块被插入到滤波器生成器、原子级更新和读出变换中，使量子增强的特征映射能够为依赖于距离的相互作用和原子能量预测做出贡献，同时保留力的能量梯度公式。该模型在8个MD17分子系统上进行评估，每个分子使用1000个训练构型。与仅能量训练相比，能量-力联合监督显著提高了能量和力的预测精度。在基准测试中，能量平均绝对误差（MAE）从2.567 kcal mol⁻¹降至0.593 kcal mol⁻¹，力MAE从16.340 kcal mol⁻¹ Å⁻¹降至1.540 kcal mol⁻¹ Å⁻¹。对乙醇的消融实验进一步表明，混合模型的性能取决于量子电路宽度、电路深度和优化稳定性之间的平衡。这些结果证明，变分量子电路可以被整合到神经力场架构中并进行端到端训练，以改善分子能量和力的预测。
英文摘要
Machine-learning force fields provide a promising route for accelerating molecular simulation by replacing expensive quantum-chemical calculations with differentiable models of molecular energies and atomic forces. However, learning accurate and energy-conserving forces remains challenging, especially when the model must capture both global energy trends and local potential-energy gradients from limited data. In this work, we propose a Hybrid Quantum SchNet architecture that integrates variational quantum circuit modules into the continuous-filter SchNet framework. Quantum modules are inserted into the filter generator, atom-wise update, and readout transformations, allowing quantum-enhanced feature mappings to contribute to distance-dependent interactions and atomic energy prediction while preserving the energy-gradient formulation of forces. The model is evaluated on eight MD17 molecular systems using 1000 training configurations per molecule. Compared with energy-only training, joint energy--force supervision substantially improves both energy and force prediction accuracy. Compared with energy-only training, joint energy--force supervision substantially improves both energy and force prediction accuracy. Averaged over the benchmark, the energy MAE decreases from 2.567 to 0.593 kcal mol$^{-1}$, while the force MAE decreases from 16.340 to 1.540 kcal mol$^{-1}$ Å$^{-1}$. Ablation experiments on ethanol further show that the performance of the hybrid model depends on the balance between quantum circuit width, circuit depth, and optimization stability. These results demonstrate that variational quantum circuits can be incorporated into neural force-field architectures and trained end-to-end to improve molecular energy and force prediction.
