使用量子启发式算法高效生成分子构象
Efficient molecular conformation generation with quantum-inspired algorithm
AI总结：
针对分子展开组合优化问题，提出量子启发式算法及紧凑相位编码方法，在QM9数据集上实现高精度构象生成，计算时间相比SA减少五倍，证明量子启发式算法在量子硬件成熟前的实用价值。
摘要：
构象生成，也称为分子展开（MU），是基于结构的药物设计中的关键步骤，仍然是一个具有挑战性的组合优化问题。量子退火（QA）在解决某些组合优化问题上，相比模拟退火（SA）等传统经典方法展现出了巨大潜力。然而，最近的一项研究表明，对于MU问题，2000量子比特的QA硬件仍然无法超越SA。在此，我们提出使用量子启发式算法来解决MU问题，以超越传统的SA。我们引入了一种高度紧凑的相位编码方法，与之前的独热编码方法相比，它可以指数级地减少表示空间。为了进行基准测试，我们在由密度泛函理论（DFT）生成的公开QM9数据集上测试了这种新方法。由我们的方法确定的构象与DFT之间的均方根偏差可以忽略不计（小于约0.5埃），这支撑了我们方法的有效性。此外，与SA相比，达到目标的中位时间指标可以减少五倍。另外，我们展示了使用量子近似优化算法（QAOA）在MindQuantum上进行模拟实验以获得最优结果。这些结果表明，即使在量子硬件成熟之前，量子启发式算法也可以应用于解决实际问题。
英文摘要：
Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly-compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5 Angstrom), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature.
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