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arXiv · 2012.07825

Analyzing the Performance of Variational Quantum Factoring on a Superconducting Quantum Processor

Amir H. Karamlou·William A. Simon·Amara Katabarwa·Travis L. Scholten·Borja Peropadre·Yudong Cao
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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.

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Amir H. Karamlou, William A. Simon, Amara Katabarwa, Travis L. Scholten, Borja Peropadre, Yudong Cao. 2020-12-14. Analyzing the Performance of Variational Quantum Factoring on a Superconducting Quantum Processor. https://doi.org/10.1038/s41534-021-00478-z

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