arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.04512quant-phphysics.optics
收藏

熵计算:开放光子系统中的一种优化范式

Entropy Computing, A Paradigm for Optimization in Open Photonic Systems

Lac Nguyen, Mohammad-Ali Miri, R. Joseph Rupert, Wesley Dyk, Sam Wu, Nick Vrahoretis, Irwin Huang, Milan Begliarbekov, Nicholas Chancellor, Uchenna Chukwu, Pran… 展开作者

Lac Nguyen, Mohammad-Ali Miri, R. Joseph Rupert, Wesley Dyk, Sam Wu, Nick Vrahoretis, Irwin Huang, Milan Begliarbekov, Nicholas Chancellor, Uchenna Chukwu, Pranav Mahamuni, Cesar Martinez-Delgado, David Haycraft, Carrie Spear, Joel Russell Huffman, Yong Meng Sua, Yu-Ping Huang

AI总结:

本文提出开放光子系统中的熵计算优化范式,构建混合光子-电子计算机,通过时间光子模式构造多能级量子比特编码概率振幅,结合电子互连编码哈密顿量,可求解多种NP-hard优化问题。

摘要:

找到组合优化问题的更优解可对物流等诸多现实应用领域产生巨大积极影响,因此学界已投入大量精力设计新型优化范式。本文展示了光学场景下该类范式的一个早期实例——熵计算范式。具体而言,我们构建了一台采用光学测量与反馈机制求解非凸优化问题的混合光子-电子计算机,通过实验验证了熵计算的可行性。该系统利用时间光子模式构造qudit(多能级量子比特),以在光子的时频自由度中编码概率振幅。此方案结合电子互连后,可将任意哈密顿量(Hamiltonian)编码进系统,求解非凸连续变量与组合优化问题。研究表明,所提出的熵计算范式可作为可扩展的通用平台,用于处理各类NP-hard优化问题。

英文摘要:

Finding better solutions to combinatorial optimization problems could have a large positive impact on many real-world application areas, such as logistics. For this reason, significant efforts have been made to design novel optimisation paradigms. Here we show an early instance of such paradigm in an optical setting, the entropy computing paradigm. Specifically, we experimentally demonstrate the feasibility of entropy computing by building a hybrid photonic-electronic computer that uses optical measurement and feedback to solve non-convex optimization problems. The system functions by using temporal photonic modes to create qudits in order to encode probability amplitudes in the time-frequency degree of freedom of a photon. This scheme, when coupled with with electronic interconnects, allows us to encode an arbitrary Hamiltonian into the system and solve non-convex continuous variables and combinatorial optimization problems. We show that the proposed entropy computing paradigm can act as a scalable and versatile platform for tackling a large range of NP-hard optimization problems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑