Analyzing the performance of variational quantum factoring on a superconducting quantum processor

• DOI: 10.1038/s41534-021-00478-z
• Publication Date: 2021-10-28T10:02:44Z
• Authors: Amir H. Karamlou; William A. Simon; Amara Katabarwa; Travis L. Scholten; Borja Peropadre; Yudong Cao
• Keywords: Quantum Physics; Physics; QC1-999; Electronic computers. Computer science; FOS: Physical sciences; QA75.5-76.95; Quantum Physics (quant-ph)

AbstractIn 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 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 ZZ-coupling between qubits as a dominant source of error in a near-term superconducting quantum processor.