CutVQA: Co-Designing Circuit Cutting and Architecture Search for Scaling Variational Quantum Algorithms

TL;DR

CutVQA introduces a co-design framework combining circuit cutting and architecture search to efficiently scale variational quantum algorithms, significantly reducing sampling overhead and training time while maintaining accuracy.

Contribution

It presents a novel co-design approach that integrates circuit cutting with quantum architecture search for scalable VQAs.

Findings

Reduces sampling overhead by 2-3 orders of magnitude.

Shortens training time by at least 50%.

Maintains baseline accuracy with the proposed method.

Abstract

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.