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arXiv · 2602.20407

Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm

Lucas A. M. Rattighieri·Pedro M. Prado·Marcos C. de Oliveira·Felipe F. Fanchini
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Abstract

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.

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Lucas A. M. Rattighieri, Pedro M. Prado, Marcos C. de Oliveira, Felipe F. Fanchini. 2026-02-23. Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm. https://arxiv.org/abs/2602.20407

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