Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem

Autor:	Venturelli, Francesco Aldo, Das, Sreetama, Caruso, Filippo
Rok vydání:	2024
Předmět:	Quantum Physics Condensed Matter - Disordered Systems and Neural Networks Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successful for solving combinatorial optimization problems (COPs). It has been observed that the optimal variational parameters obtained from one instance of a COP can be transferred to another instance, producing sufficiently satisfactory solutions for the latter. In this context, a suitable method for further improving the solution is to fine-tune a subset of the transferred parameters. We numerically explore the role of optimizing individual QAOA layers in improving the approximate solution of the Max-Cut problem after parameter transfer. We also investigate the trade-off between a good approximation and the required optimization time when optimizing transferred QAOA parameters. These studies show that optimizing a subset of layers can be more effective at a lower time-cost compared to optimizing all layers. Comment: 8 pages, 6 figures. Comments are welcome
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.21071 Zobrazit plný text záznamu View this record from Arxiv