用生成模型加速量子化学模拟，提升药物研发效率。
Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations
- 结合生成模型与量子算法，优化分子能级计算流程。
- 在8种分子上验证，误差可控且计算资源仅需现有方法的几分之一。
- 适合从事量子计算制药或高效模拟的研究者参考。
蛋白质-配体体系的结合能计算需精确处理电子结构，该问题在经典硬件上呈指数级增长，而当前量子硬件噪声过大，难以支撑所需电路深度。本文提出一种混合量子-经典工作流，基于富士通FX700理想态向量模拟器和QARP框架，解决了量子采样对角化流程中的两项结构性低效。首先，在QSCI框架中引入线性扩展CNOT UCCSD（LCNot-UCCSD）变分族，将原有LUCJ方法中$/mathcal{O}(N^6)$的CCSD参数初始化替换为$/mathcal{O}(N^4)$的MP2振幅初始化；其次，提出QSCI-RBM，以受限玻尔兹曼机（RBM）替代SQD框架中的组态重构，作为紧凑的生成子空间扩展模型。在STO-3G基组下对8种分子、14种人工误差水平（每种100次独立运行）进行评估，并在cc-pVDZ基组下验证了氮气分子势能面扫描结果。进一步嵌入于DMET框架，应用于已获批抗病毒药阿米达拉汀（C$_{10}$H$_{17}$N，11个DMET片段）及新冠病毒主蛋白酶-共价抑制剂卡莫夫鲁复合物（PDB: 7BUY，C$_{15}$H$_{28}$N$_4$O$_5$S，10个片段）。据我们所知，这是首次在量子模拟器上部署LCNot-UCCSD于QSCI框架，也是首个将DMET-QSCI(LCNot-UCCSD)-RBM应用于工业相关蛋白-配体系统的实例。相比克利夫兰诊所、理研及IBM Quantum的最先进工作，本方法仅需极少的古典计算资源，显著提升药物发现模拟的效率与经济性。
原文摘要 · Abstract (English)
Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit depths. This report presents a hybrid quantum-classical workflow performed on the Fujitsu FX700 ideal state-vector simulator using QARP that addresses two structural inefficiencies in quantum-sampling-based diagonalization workflows. First, we integrate the Linear Scaling CNOT UCCSD (LCNot-UCCSD) ansatz into the QSCI framework, replacing the $\mathcal{O}(N^6)$ CCSD parameter initialization of the competing LUCJ ansatz approach with $\mathcal{O}(N^4)$ MP2-amplitude initialization. Second, we introduce QSCI-RBM, a variant that replaces the configuration recovery of the SQD framework with a Restricted Boltzmann Machine (RBM) acting as a compact generative subspace expansion model. Both are evaluated on eight different molecules in STO-3G across 14 controlled artificial error levels with 100 independent runs each, validated on potential energy surface scans of the N$_2$ molecule in cc-pVDZ, and embedded within DMET to treat the FDA-approved antiviral Amantadine (C$_{10}$H$_{17}$N, 11 DMET fragments) and the active region of the SARS-CoV-2 main protease complexed with its covalent inhibitor Carmofur (PDB: 7BUY, C$_{15}$H$_{28}$N$_4$O$_5$S, 10 fragments). To our knowledge, this is the first deployment of LCNot-UCCSD within QSCI on a quantum computing simulator, and the first DMET-QSCI(LCNot-UCCSD)-RBM application to an industry-relevant protein-ligand system. By utilizing a fraction of the classical computing resources required by the current state-of-the-art work by Cleveland Clinic, RIKEN, and IBM Quantum, this approach enables more efficient and economical drug discovery simulations for the industry.
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