Anchor: Reducing Temporal and Spatial Output Performance Variability on Quantum Computers
Abstract
Quantum computing, which has the power to accelerate many computing applications, is currently a technology under development. As a result, the existing noisy intermediate-scale quantum (NISQ) computers suffer from different hardware noise effects, which cause errors in the output of quantum programs. These errors cause a high degree of variability in the performance (i.e., output fidelity) of quantum programs, which varies from one computer to another and from one day to another. Consequently, users are unable to get consistent results even when running the same program multiple times. Current solutions, while focusing on reducing the errors faced by quantum programs, do not address the variability challenge. To address this challenge, we propose Anchor, a first-of-its-kind technique that leverages linear programming to reduce the performance variability by 73% on average over the state-of-the-art implementation focused on error reduction.
Keywords
Cite
@article{arxiv.2510.06172,
title = {Anchor: Reducing Temporal and Spatial Output Performance Variability on Quantum Computers},
author = {Yuqian Huo and Daniel Leeds and Jason Ludmir and Nicholas S. DiBrita and Tirthak Patel},
journal= {arXiv preprint arXiv:2510.06172},
year = {2025}
}
Comments
This paper will appear in the Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2026