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

• URL: http://arxiv.org/abs/2412.21071v1
• Date: Mon, 30 Dec 2024 16:41:16 GMT
• Title: Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
• Authors: Francesco Aldo Venturelli, Sreetama Das, Filippo Caruso,
• Abstract summary: We numerically explore the role of individual QAOA layers in improving the approximate solution of the Max-Cut problem after parameter transfer. These studies show that optimizing a subset of layers can be more effective at a lower time-cost compared to optimizing all layers.
• Score: 1.515687944002438
• License:
• Abstract: 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.

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