Accelerating Feedback-based Algorithms for Quantum Optimization Using Gradient Descent

• URL: http://arxiv.org/abs/2602.12387v1
• Date: Thu, 12 Feb 2026 20:30:53 GMT
• Title: Accelerating Feedback-based Algorithms for Quantum Optimization Using Gradient Descent
• Authors: Masih Mozakka, Mohsen Heidari,
• Abstract summary: Quantum Lyapunov Control (QLC) employs feedback-driven control laws that guarantee monotonic non-decreasing objective values.<n>We propose a hybrid method that incorporates per-layer gradient estimation to accelerate the convergence of QLC.
• Score: 6.852394426719304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Feedback-based methods have gained significant attention as an alternative training paradigm for the Quantum Approximate Optimization Algorithm (QAOA) in solving combinatorial optimization problems such as MAX-CUT. In particular, Quantum Lyapunov Control (QLC) employs feedback-driven control laws that guarantee monotonic non-decreasing objective values, can substantially reduce the training overhead of QAOA, and mitigate barren plateaus. However, these methods might require long control sequences, leading to sub-optimal convergence rates. In this work, we propose a hybrid method that incorporates per-layer gradient estimation to accelerate the convergence of QLC while preserving its low training overhead and stability guarantees. By leveraging layer-wise gradient information, the proposed approach selects near-optimal control parameters, resulting in significantly faster convergence and improved robustness. We validate the effectiveness of the method through extensive numerical experiments across a range of problem instances and optimization settings.

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