CutVQA: Co-Designing Circuit Cutting and Architecture Search for Scaling Variational Quantum Algorithms
This work addresses the challenge of executing large quantum circuits on small NISQ devices for quantum computing researchers, presenting a co-design approach that is incremental but offers significant practical improvements.
The paper tackles the problem of high sampling overhead in circuit cutting for scaling variational quantum algorithms (VQAs) by introducing CutVQA, a co-design framework that integrates circuit cutting with quantum architecture search. Results show that CutVQA matches baseline accuracy while reducing sampling overhead by 2-3 orders of magnitude and shortening training time by at least 50%.
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.