使用Ising机在化学反应网络中寻找最优路径
Finding Optimal Pathways in Chemical Reaction Networks Using Ising Machines
AI总结：
针对化学反应网络最优路径寻找的组合优化难题，首次提出基于Ising机的求解方案，结合贝叶斯优化与惩罚项分组调优技术，实验验证了近似最优路径求解的可行性。
摘要：
在化学反应网络中寻找最优路径对于阐明和设计化学过程至关重要，在合成规划、代谢通路分析等领域具有重要应用价值。这类化学路径寻找问题可被建模为带约束的组合优化问题，目标是在给定网络中找到连接起始原料与目标产物的最优化学反应组合。由于组合爆炸效应，寻找最优路径所需的计算时间会随网络规模呈指数级增长。包括量子退火装置与模拟退火装置在内的Ising机，是专门用于求解这类困难组合优化问题的新型计算设备，应用前景广阔。但据我们所知，目前尚未有将Ising机应用于化学路径寻找问题的尝试。在本文中，我们提出了首个将Ising/量子计算应用于化学路径寻找问题的方案。由化学路径寻找问题转化得到的Ising模型包含多种对应约束违反情况的惩罚项，而如何设置不同类型惩罚项的合适强度并非易事。为解决这一挑战，我们采用贝叶斯优化进行参数调优。此外，我们提出了一种新技术，通过根据底层问题结构对惩罚项进行分组来提升调优性能。我们使用D-Wave Advantage系统与模拟退火方法对所提算法进行了性能评估与分析。基准测试结果揭示了寻找精确最优路径面临的挑战，同时结果也表明，在可接受一定程度成本值相对误差的前提下，寻找近似最优路径是可行的。
英文摘要：
Finding optimal pathways in chemical reaction networks is essential for elucidating and designing chemical processes, with significant applications such as synthesis planning and metabolic pathway analysis. Such a chemical pathway-finding problem can be formulated as a constrained combinatorial optimization problem, aiming to find an optimal combination of chemical reactions connecting starting materials to target materials in a given network. Due to combinatorial explosion, the computation time required to find an optimal pathway increases exponentially with the network size. Ising machines, including quantum and simulated annealing devices, are promising novel computers dedicated to such hard combinatorial optimization. However, to the best of our knowledge, there has yet to be an attempt to apply Ising machines to chemical pathway-finding problems. In this article, we present the first Ising/quantum computing application for chemical pathway-finding problems. The Ising model, translated from a chemical pathway-finding problem, involves several types of penalty terms for violating constraints. It is not obvious how to set appropriate penalty strengths of different types. To address this challenge, we employ Bayesian optimization for parameter tuning. Furthermore, we introduce a novel technique that enhances tuning performance by grouping penalty terms according to the underlying problem structure. The performance evaluation and analysis of the proposed algorithm were conducted using a D-Wave Advantage system and simulated annealing. The benchmark results reveal challenges in finding exact optimal pathways. Concurrently, the results indicate the feasibility of finding approximate optimal pathways, provided that a certain degree of relative error in cost value is acceptable.
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