Analyzing the performance of variational quantum factoring on a superconducting quantum processor
Abstract
In the nearterm, hybrid quantumclassical 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 QAOAbased quantum optimization approach 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 ZZcoupling between qubits as a dominant source of error in a nearterm superconducting quantum processor.
 Publication:

npj Quantum Information
 Pub Date:
 2021
 DOI:
 10.1038/s4153402100478z
 arXiv:
 arXiv:2012.07825
 Bibcode:
 2021npjQI...7..156K
 Keywords:

 Quantum Physics
 EPrint:
 npj Quantum Inf 7, 156 (2021)