Accelerating Feedback-Based Quantum Algorithms through Time Rescaling

• URL: http://arxiv.org/abs/2504.01256v1
• Date: Wed, 02 Apr 2025 00:05:01 GMT
• Title: Accelerating Feedback-Based Quantum Algorithms through Time Rescaling
• Authors: L. A. M. Rattighieri, G. E. L. Pexe, B. L. Bernado, F. F. Fanchini,
• Abstract summary: We introduce TR-FQA and TR-FALQON, time-rescaled versions of FQA and FALQON, respectively.<n>The results show that TR-FALQON accelerates convergence to the optimal solution in the early layers of the circuit.<n>In the context of state preparation, TR-FQA demonstrates superior convergence, reducing the required circuit depth by several hundred layers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work investigates the impact of time rescaling on the performance of Feedback Quantum Algorithms (FQA) and their variant for optimization tasks, FALQON. We introduce TR-FQA and 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 ANNNI quantum many-body model. The results show that TR-FALQON accelerates convergence to the optimal solution in the early layers of the circuit, significantly outperforming its standard counterpart in shallow-depth regimes. In the context of state preparation, TR-FQA demonstrates superior convergence, reducing the required circuit depth by several hundred layers. These findings highlight the potential of time rescaling as a strategy to enhance algorithmic performance on near-term quantum devices.

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