Transfer-Based Strategies for Multi-Target Quantum Optimization

TL;DR

This paper explores transfer strategies to improve multi-target quantum optimization, significantly reducing iterations needed while maintaining solution quality, thus advancing scalable quantum algorithms.

Contribution

Introduces a two-stage transfer framework with novel methods like warm-start, Taylor-based estimation, hierarchical clustering, and deep learning for multi-target quantum optimization.

Findings

Transfer techniques reduce optimization iterations.

Methods maintain acceptable cost values.

Framework demonstrates scalability in quantum optimization.

Abstract

We address the challenge of multi-target quantum optimization, where the objective is to simultaneously optimize multiple cost functions defined over the same quantum search space. To accelerate optimization and reduce quantum resource usage, we investigate a range of strategies that enable knowledge transfer between related tasks. Specifically, we introduce a two-stage framework consisting of a training phase where solutions are progressively shared across tasks and an inference phase, where unoptimized targets are initialized based on prior optimized ones. We propose and evaluate several methods, including warm-start initialization, parameter estimation via first-order Taylor expansion, hierarchical clustering with D-level trees, and deep learning-based transfer. Our experimental results, using parameterized quantum circuits implemented with PennyLane, demonstrate that transfer techniques significantly reduce the number of required iterations while maintaining an acceptable cost value. These findings highlight the promise of multi-target generalization in quantum optimization pipelines and provide a foundation for scalable multi-target quantum optimization.