论文标题
量子启发的张量网络方法，用于约束组合优化问题
A quantum-inspired tensor network method for constrained combinatorial optimization problems
论文作者
论文摘要
对于理论研究和现实世界应用，组合优化都是一般兴趣。快速开发的量子算法为解决组合优化问题提供了不同的观点。在本文中，我们提出了一种基于量子启发的张量 - 网络算法，用于一般局部约束的组合优化问题。我们的算法为感兴趣的问题构建了哈密顿量，有效地将其映射到量子问题，然后将约束直接编码为张量网络状态，并通过将系统发展到汉密尔顿的基态来解决最佳解决方案。我们通过开放式挖掘问题证明了我们的算法，这导致了二次渐近时间复杂性。我们的数值结果表明，这种构建的有效性和潜在的应用在进一步的一般组合优化问题中的进一步研究中。
Combinatorial optimization is of general interest for both theoretical study and real-world applications. Fast-developing quantum algorithms provide a different perspective on solving combinatorial optimization problems. In this paper, we propose a quantum-inspired tensor-network-based algorithm for general locally constrained combinatorial optimization problems. Our algorithm constructs a Hamiltonian for the problem of interest, effectively mapping it to a quantum problem, then encodes the constraints directly into a tensor network state and solves the optimal solution by evolving the system to the ground state of the Hamiltonian. We demonstrate our algorithm with the open-pit mining problem, which results in a quadratic asymptotic time complexity. Our numerical results show the effectiveness of this construction and potential applications in further studies for general combinatorial optimization problems.
