Report
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
| العنوان: | Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem |
|---|---|
| المؤلفون: | Venturelli, Francesco Aldo, Das, Sreetama, Caruso, Filippo |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Condensed Matter Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Computer Science - Machine Learning |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2412.21071 |
| رقم الانضمام: | edsarx.2412.21071 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |