Enhanced Quantum Circuit Cutting Framework for Sampling Overhead Reduction

TL;DR

This paper introduces ShotQC, an improved quantum circuit cutting framework that reduces sampling overhead by adaptively allocating resources and optimizing postprocessing, enabling more efficient use of NISQ hardware.

Contribution

ShotQC presents novel optimizations in shot distribution and cut parameterization, significantly lowering sampling overhead in quantum circuit cutting techniques.

Findings

Reduces sampling overhead in circuit cutting

Maintains classical postprocessing complexity

Effective across various benchmark circuits

Abstract

The recently developed quantum circuit cutting technique greatly extends the capabilities of current noisy intermediate-scale quantum (NISQ) hardware. However, it introduces substantial overhead in both classical postprocessing and quantum resources, as the postprocessing complexity and sampling cost scale exponentially with the number of circuit cuts. In this work, we propose an enhanced circuit cutting framework, ShotQC, which effectively reduces the sampling overhead through two key optimizations: shot distribution and cut parameterization. The former employs an adaptive Monte Carlo strategy to dynamically allocate more quantum resources to subcircuit configurations that contribute more to the variance in the final outcome. The latter exploits additional degrees of freedom in postprocessing to further suppress variance. Integrating these optimizations, ShotQC significantly reduces the sampling overhead without increasing classical postprocessing complexity, as demonstrated across a range of benchmark circuits.