Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping
We present Noise-Directed Adaptive Remapping (NDAR), a heuristic algorithm for approximately solving binary optimization problems by leveraging certain types of noise. We consider access to a noisy quantum processor with dynamics that features a global attractor state. In a standard setting, such no...
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| Автори: | , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2025-11-01
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| Серія: | Quantum |
| Онлайн доступ: | https://quantum-journal.org/papers/q-2025-11-06-1906/pdf/ |
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