Circuit cutting enables large quantum circuits to run on small NISQ devices, but it introduces an exponentially high sampling overhead. Here, we present CutVQA, a co-design framework that integrates circuit cutting with quantum architecture search to scale VQAs. CutVQA performs cutting-aware architecture search and applies subcircuit-level optimization enabled by parameter locality, reducing both reconstruction and training overhead. Evaluations on two representative VQAs (QAOA and VQE) show that CutVQA matches baseline accuracy while reducing sampling overhead by 2-3 orders of magnitude and shortening training time by at least 50%, demonstrating that co-design is essential for scaling VQA execution.
@article{arxiv.2508.03376,
title = {CutVQA: Co-Designing Circuit Cutting and Architecture Search for Scaling Variational Quantum Algorithms},
author = {Jun Wu and Jicun Li and Jiaqi Yang and Wei Xie and Xiang-Yang Li},
journal= {arXiv preprint arXiv:2508.03376},
year = {2026}
}