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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.04253 |
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| _version_ | 1866909014915284992 |
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| author | Thomaz, Gabriel Fernandes Monteiro, Eduarda Rodrigues Marchi, Jerusa Pretto, Marcelo Zen Fumaco, Alisson dos Passos da Rosa, Evandro Chagas Ribeiro |
| author_facet | Thomaz, Gabriel Fernandes Monteiro, Eduarda Rodrigues Marchi, Jerusa Pretto, Marcelo Zen Fumaco, Alisson dos Passos da Rosa, Evandro Chagas Ribeiro |
| contents | The Feedback-based Algorithm for Quantum Optimization (FALQON) offers a deterministic alternative to variational quantum algorithms by bypassing classical optimization loops. However, maintaining convergence on large problem instances often requires restricting the time step, necessitating quantum circuit depths that exceed Noisy Intermediate-Scale Quantum (NISQ) hardware capabilities. This paper investigates the parameter transferability of second-order FALQON applied to the Max-Cut problem on 3-regular graphs. Through numerical experiments evaluating quantum circuits up to 16 layers on graphs up to 24 nodes, we demonstrate a highly advantageous scaling behavior: transferring feedback parameters optimized on small instances to larger target graphs yields significantly higher approximation ratios than natively optimizing the parameters directly on the larger graphs. This performance advantage arises because parameters trained on smaller instances can safely adopt aggressively larger time steps. By offloading the expensive parameter discovery phase to small-scale instances, this transfer strategy simultaneously reduces computational overhead and enhances the approximation ratio, thereby bringing FALQON closer to practical viability on near-term quantum architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04253 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Second-Order FALQON Parameter Transfer for the Max-Cut Problem on 3-Regular Graphs Thomaz, Gabriel Fernandes Monteiro, Eduarda Rodrigues Marchi, Jerusa Pretto, Marcelo Zen Fumaco, Alisson dos Passos da Rosa, Evandro Chagas Ribeiro Emerging Technologies Quantum Physics The Feedback-based Algorithm for Quantum Optimization (FALQON) offers a deterministic alternative to variational quantum algorithms by bypassing classical optimization loops. However, maintaining convergence on large problem instances often requires restricting the time step, necessitating quantum circuit depths that exceed Noisy Intermediate-Scale Quantum (NISQ) hardware capabilities. This paper investigates the parameter transferability of second-order FALQON applied to the Max-Cut problem on 3-regular graphs. Through numerical experiments evaluating quantum circuits up to 16 layers on graphs up to 24 nodes, we demonstrate a highly advantageous scaling behavior: transferring feedback parameters optimized on small instances to larger target graphs yields significantly higher approximation ratios than natively optimizing the parameters directly on the larger graphs. This performance advantage arises because parameters trained on smaller instances can safely adopt aggressively larger time steps. By offloading the expensive parameter discovery phase to small-scale instances, this transfer strategy simultaneously reduces computational overhead and enhances the approximation ratio, thereby bringing FALQON closer to practical viability on near-term quantum architectures. |
| title | Second-Order FALQON Parameter Transfer for the Max-Cut Problem on 3-Regular Graphs |
| topic | Emerging Technologies Quantum Physics |
| url | https://arxiv.org/abs/2605.04253 |