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

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.

Figures (8)

• Figure 1: Examples of circuit cutting techniques.
• Figure 2: Algorithmic performance versus sampling overhead for various QAOA ansätze on a MaxCut instance. H.P.: High Performance; L.P.: Low Performance; H.S.: High Sampling Overhead; L.S.: Low Sampling.
• Figure 3: Schematic overview of the CutVQA framework. The quantum architecture search module selects ansätze that balance algorithmic performance with circuit-cutting overhead. The chosen ansatz is partitioned into subcircuits, executed independently, and recombined via classical post-processing. Parameter optimization is localized to individual subcircuits, reducing computational cost and enhancing training efficiency on near-term quantum hardware.
• Figure 4: Efficiency of CutVQA’s parameter-localized optimization.
• Figure 5: Sampling overhead vs. ansatz depth.