| Texto completo | |
| Autor(es): |
Rattighieri, L. A. M.
;
Pexe, G. E. L.
;
Bernardo, B. L.
;
Fanchini, F. F.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PHYSICAL REVIEW A; v. 112, n. 4, p. 10-pg., 2025-10-07. |
| Resumo | |
This work investigates the impact of time rescaling on the performance of Feedback-Based Quantum Algorithms (FQAs) and their variant for optimization tasks, Feedback-Based Algorithm for Quantum Optimization (FALQON). We introduce the Time-Rescaled Feedback-Based Quantum Algorithm (TR-FQA) and TimeRescaled Feedback-Based Algorithm for Quantum Optimization (TR-FALQON), time-rescaled versions of FQA and FALQON, respectively. The method is applied to two representative problems: the MaxCut combinatorial optimization problem and ground-state preparation in the axial next-nearest neighbor Ising (ANNNI) model. The results show that TR-FALQON accelerates convergence toward the optimal solution in the circuit's early layers and outperforms the standard version in low-depth regimes. In the context of state preparation, TR-FQA requires fewer layers than the original algorithm. These findings suggest that time rescaling can reduce the number of layers needed to reach a high-quality solution, even if the exact ground state is not obtained. (AU) | |
| Processo FAPESP: | 23/04987-6 - Otimização e aprendizado de máquina quântico: algoritmos variacionais e aplicações |
| Beneficiário: | Felipe Fernandes Fanchini |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 24/00998-6 - Centro de Pesquisa e Inovação de Materiais Inteligentes e Quânticos (CRISQuaM) |
| Beneficiário: | Daniel Mario Ugarte |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |