An efficient quantum proactive incremental learning algorithm  

作　　者：Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao 

机构地区：[1]State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China [2]School of Cyberspace Security,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2025年第1期45-53,共9页中国科学:物理学、力学、天文学(英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.62272048,61976056,and 62371069);BUPT Excellent Ph.D.Students Foundation(Grant No.CX20241055)。

摘　　要：In scenarios where a large amount of data needs to be learned,incremental learning can make full use of old knowledge,signif-icantly reduce the computational cost of the overall learning process,and maintain high performance.In this paper,taking the MaxCut problem as our example,we introduce the idea of incremental learning into quantum computing,and propose a Quantum Proactive Incremental Learning algorithm(QPIL).Instead of a one-off training of quantum circuit,QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices,proactively reducing large-scale problems to smaller ones to solve in steps,providing an efficient solution for MaxCut.Specifically,some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first.Then,in each incremental phase,the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase.We perform experiments on 120 different small-scale graphs,and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR),time cost,anti-forgetting,and solv-ing stability.In particular,QPIL’s AR surpasses 20%of mainstream quantum baselines,and the time cost is less than 1/5 of them.The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale MaxCut and other combinatorial optimization problems.

关 键 词：variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing 

分 类 号：O413[理学—理论物理] TP18[理学—物理]