Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
This is an incremental improvement for quantum computing researchers working on combinatorial optimization with NISQ devices.
The paper tackles the problem of improving solution quality and reducing optimization time for the Max-Cut problem using QAOA by exploring layer-selective fine-tuning of transferred parameters, showing that optimizing a subset of layers can be more effective at lower time-cost compared to optimizing all layers.
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.