这篇论文用生成式ML(RBM)和优化初始化让量子分子模拟更省计算资源，能处理实际药物分子比如抗病毒药和新冠病毒蛋白酶。
该论文提出一个混合量子-经典工作流，在Fujitsu FX700理想态矢量模拟器上使用QARP运行。它用O(N^4) MP2振幅初始化的LCNot-UCCSD ansatz替代O(N^6) CCSD初始化，并引入QSCI-RBM以RBM替代SQD进行配置恢复。方法在8种分子的STO-3G基组上进行14个误差等级各100次独立运行，并在cc-pVDZ基组的N2分子势能面扫描和DMET嵌入的Amantadine（C10H17N，11个片段）及SARS-CoV-2主蛋白酶-Carmofur复合物（10个片段）上验证。这是首次在量子模拟器上将LCNot-UCCSD部署于QSCI，也是首次将DMET-QSCI(LCNot-UCCSD)-RBM应用于工业相关蛋白-配体系统，计算资源需求低于Cleveland Clinic等先前工作。
Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations
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.