# Accelerating Feedback-based Algorithms for Quantum Optimization Using Gradient Descent

arXiv: https://arxiv.org/abs/2602.12387 · HTML: https://mlsanity.com/paper/2602.12387/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [A Survey of Quantum Lyapunov Control Methods](https://mlsanity.com/paper/1304.3997/) (2013)

## Abstract (arXiv)

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.