发表机构
Qclairvoyance Quantum Labs; The University of Arizona; Indian Institute of Technology Bombay（Qclairvoyance量子实验室; 亚利桑那大学; 印度理工学院孟买分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对量子选择配置相互作用中现有技术问题，提出基于受限玻尔兹曼机的QSCI - RBM协议并集成到DMET框架，应用于蛋白质 - 配体复合物模拟，能以更小配置子空间达化学精度，减少经典计算开销，实现复杂生物系统可扩展量子嵌入模拟。
AI 中文摘要
基于样本的量子对角化（SQD）是量子选择配置相互作用（QSCI）的扩展，是计算分子基态能量的有前景的量子 - 经典混合范式。QSCI利用量子采样避免了贫瘠高原并能直接重建相关电子波函数。但现有配置恢复技术主要施加对称约束，不能保证最优选择最相关配置，导致子空间不必要地大且经典对角化成本增加。本文引入基于受限玻尔兹曼机（RBM）的机器学习紧凑子空间生成协议QSCI - RBM，并将其集成到密度矩阵嵌入理论（DMET）框架中。RBM在量子采样配置上训练以学习主导行列式的潜在概率分布，实现高概率配置的有针对性生成。将此框架应用于模拟涉及卡莫氟与SARS-CoV-2主要蛋白酶（$M^{\text{pro}}$）结合的蛋白质 - 配体复合物。结果表明，DMET - QSCI - RBM仅访问约4%的配置子空间就能达到化学精度阈值内的能量。相比之下，标准DMET - SQD模拟即使化学势几乎收敛，访问高达20%的子空间也未能达到化学精度。这些发现突出了RBM辅助配置生成在保持物理精度的同时产生显著更紧凑的子空间，从而减少经典计算开销并实现复杂生物系统的可扩展量子嵌入模拟。
英文摘要
Sample-based Quantum Diagonalization (SQD), an extension of Quantum Selected Configuration Interaction (QSCI), has emerged as a promising hybrid quantum-classical paradigm for computing molecular ground state energies. By leveraging quantum sampling instead of variational optimization, QSCI avoids barren plateaus and enables direct reconstruction of correlated electronic wavefunctions. However, existing configuration recovery techniques primarily enforce symmetry constraints without guaranteeing optimal selection of the most physically relevant configurations, often leading to unnecessarily large subspaces and increased classical diagonalization costs. In this work, we introduce a machine-learned compact subspace generation protocol based on Restricted Boltzmann Machines (RBMs), termed QSCI-RBM, and integrate it within the Density Matrix Embedding Theory (DMET) framework. The RBM is trained on quantum-sampled configurations to learn the underlying probability distribution of dominant determinants, enabling the targeted generation of high-probability configurations. We apply this framework to the simulation of a protein-ligand complex involving the inhibitor Carmofur bound to the SARS-CoV-2 main protease ($M^{\text{pro}}$). Our results demonstrate that DMET-QSCI-RBM achieves energies within the chemical accuracy threshold by accessing only approximately 4% of the configuration subspace. In contrast, standard DMET-SQD simulations failed to reach chemical accuracy while accessing up to 20% of the subspace, even as the chemical potential itself nearly converged. These findings highlight that RBM-assisted configuration generation produces significantly more compact subspaces while preserving physical accuracy, thereby reducing classical computational overhead and enabling the scalable quantum embedding simulation of complex biological systems.
Comments31 pages, 16 figures
