.png){kind=icon}
返回
An efficient quantum proactive incremental learning algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s11433-024-2501-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In scenarios where a large amount of data needs to be learned, incremental learning can make full use of old knowledge, significantly 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 solving 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.
Keyword:
variational quantum algorithm
incremental learning
multi-phase training
MaxCut
quantum computing
期刊
S
IF:
7.5
论文数:
3.9K
被引数:
7.4K
机构
引用论文
.png){kind=icon}
分享收藏
Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation近期量子计算技术: 变分量子算法，误差缓解，电路编译，基准测试和经典仿真
PREAI
分享收藏A general quantum minimum searching algorithm with high success rate and its implementation一种通用的高成功率量子最小搜索算法及其实现
PREAI
分享收藏
查看更多内容