融合量子计算与机器学习，突破药物研发的精度与效率瓶颈。
The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
- 结合HPC、ML与量子计算，构建高精度分子模拟新范式。
- 利用混合量子-经典架构，实现化学精度模拟且突破经典近似限制。
- 适合关注量子增强药物设计与下一代材料研发的研究者。
将量子力学融入药物发现标志着从经验试错向定量精确的重大转变。然而，从头算分子动力学的高昂成本长期迫使化学精度与计算可扩展性之间做出妥协。本文指出，高性能计算（HPC）、机器学习（ML）与量子计算（QC）的融合是解决这一瓶颈的关键。尽管如FeNNix-Bio1等机器学习基础模型能够实现量子级精度模拟，仍受限于经典数据生成的固有局限。本文详述了采用混合量子处理单元（QPU）-GPU架构的高性能量子计算（HPQC）如何成为量子化学数据的终极加速器。通过希尔伯特空间映射，该系统可在避免经典近似启发式方法的前提下实现真正的化学精度。我们展示了三者融合如何优化从系统准备到机器学习驱动的高保真模拟的整个药物发现流程。最后，我们将量子增强采样定位为超越GPU的前沿，用于建模反应性细胞系统及开创下一代材料。
原文摘要 · Abstract (English)
Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio molecular dynamics has historically forced a compromise between chemical accuracy and computational scalability. This paper identifies the convergence of High-Performance Computing (HPC), Machine Learning (ML), and Quantum Computing (QC) as the definitive solution to this bottleneck. While ML foundation models, such as FeNNix-Bio1, enable quantum-accurate simulations, they remain tethered to the inherent limits of classical data generation. We detail how High-Performance Quantum Computing (HPQC), utilizing hybrid QPU-GPU architectures, will serve as the ultimate accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。