Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Chen, Po-Hung, Chiou, Dah-Wei, Chen, Bo-Hung, Jiang, Jie-Hong Roland
Institutions: National Taiwan University, The University of Texas at Austin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405768321