Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910031216115712 |
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| author | Rattighieri, Lucas A. M. Prado, Pedro M. de Oliveira, Marcos C. Fanchini, Felipe F. |
| author_facet | Rattighieri, Lucas A. M. Prado, Pedro M. de Oliveira, Marcos C. Fanchini, Felipe F. |
| contents | Limited circuit depth remains a central constraint for quantum optimization in the noisy intermediate-scale quantum (NISQ) regime, where shallow unitary dynamics may fail to sufficiently concentrate probability on low-energy configurations. We introduce Measurement-Guided Initialization (MGI), an iterative strategy that uses measurement outcomes from previous executions to update the initialization of subsequent runs. The method extracts single-qubit marginal probabilities from dominant measurement outcomes and prepares a biased product-state initialization, allowing information obtained during optimization to be reused without introducing classical parameter optimization. We implement this approach in the context of the Feedback-Based Algorithm for Quantum Optimization (FALQON) and evaluate its performance on weighted MaxCut instances. Numerical results show that measurement-guided initialization improves the performance of shallow-depth circuits and enables iterative refinement toward high-quality solutions while preserving the non-variational structure of the algorithm. These results indicate that measurement statistics can be exploited to improve shallow quantum optimization protocols compatible with NISQ devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20407 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm Rattighieri, Lucas A. M. Prado, Pedro M. de Oliveira, Marcos C. Fanchini, Felipe F. Quantum Physics Limited circuit depth remains a central constraint for quantum optimization in the noisy intermediate-scale quantum (NISQ) regime, where shallow unitary dynamics may fail to sufficiently concentrate probability on low-energy configurations. We introduce Measurement-Guided Initialization (MGI), an iterative strategy that uses measurement outcomes from previous executions to update the initialization of subsequent runs. The method extracts single-qubit marginal probabilities from dominant measurement outcomes and prepares a biased product-state initialization, allowing information obtained during optimization to be reused without introducing classical parameter optimization. We implement this approach in the context of the Feedback-Based Algorithm for Quantum Optimization (FALQON) and evaluate its performance on weighted MaxCut instances. Numerical results show that measurement-guided initialization improves the performance of shallow-depth circuits and enables iterative refinement toward high-quality solutions while preserving the non-variational structure of the algorithm. These results indicate that measurement statistics can be exploited to improve shallow quantum optimization protocols compatible with NISQ devices. |
| title | Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2602.20407 |