Analysis of The Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in Presence of Noisy Channels
arxiv(2022)
摘要
The vehicle routing problem (VRP) is an NP-hard optimization problem that has
been an interest of research for decades in science and industry. The objective
is to plan routes of vehicles to deliver goods to a fixed number of customers
with optimal efficiency. Classical tools and methods provide good
approximations to reach the optimal global solution. Quantum computing and
quantum machine learning provide a new approach to solving combinatorial
optimization of problems faster due to inherent speedups of quantum effects.
Many solutions of VRP are offered across different quantum computing platforms
using hybrid algorithms such as quantum approximate optimization algorithm and
quadratic unconstrained binary optimization. In this work, we build a basic VRP
solver for 3 and 4 cities using the variational quantum eigensolver on a fixed
ansatz. The work is further extended to evaluate the robustness of the solution
in several examples of noisy quantum channels. We find that the performance of
the quantum algorithm depends heavily on what noise model is used. In general,
noise is detrimental, but not equally so among different noise sources.
更多查看译文
关键词
Combinatorial optimization (CO),Ising model,quantum noise channels,variational quantum eigensolver (VQE)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要