Analyzing the Performance of Variational Quantum Factoring on a Superconducting Quantum Processor
Abstract
In the near-term, hybrid quantum-classical algorithms hold great potential for outperforming classical approaches. Understanding how these two computing paradigms work in tandem is critical for identifying areas where such hybrid algorithms could provide a quantum advantage. In this work, we study a QAOA-based quantum optimization algorithm by implementing the Variational Quantum Factoring (VQF) algorithm. We execute experimental demonstrations using a superconducting quantum processor and investigate the trade-off between quantum resources (number of qubits and circuit depth) and the probability that a given biprime is successfully factored. In our experiments, the integers 1099551473989, 3127, and 6557 are factored with 3, 4, and 5 qubits, respectively, using a QAOA ansatz with up to 8 layers and we are able to identify the optimal number of circuit layers for a given instance to maximize success probability. Furthermore, we demonstrate the impact of different noise sources on the performance of QAOA and reveal the coherent error caused by the residual ZZ-coupling between qubits as a dominant source of error in the superconducting quantum processor.
Cite
@article{arxiv.2012.07825,
title = {Analyzing the Performance of Variational Quantum Factoring on a Superconducting Quantum Processor},
author = {Amir H. Karamlou and William A. Simon and Amara Katabarwa and Travis L. Scholten and Borja Peropadre and Yudong Cao},
journal= {arXiv preprint arXiv:2012.07825},
year = {2021}
}