arXiv Analytics

Sign in

arXiv:1703.06613 [quant-ph]AbstractReferencesReviewsResources

Solving Systems of Linear Equations with a Superconducting Quantum Processor

Yarui Zheng, Chao Song, Ming-Cheng Chen, Benxiang Xia, Wuxin Liu, Qiujiang Guo, Libo Zhang, Da Xu, Hui Deng, Keqiang Huang, Yulin Wu, Zhiguang Yan, Dongning Zheng, Li Lu, Jian-Wei Pan, H. Wang, Chao-Yang Lu, Xiaobo Zhu

Published 2017-03-20Version 1

Superconducting quantum circuits are promising candidate for building scalable quantum computers. Here, we use a four-qubit superconducting quantum processor to solve a two-dimensional system of linear equations based on a quantum algorithm proposed by Harrow, Hassidim, and Lloyd [Phys. Rev. Lett. \textbf{103}, 150502 (2009)], which promises an exponential speedup over classical algorithms under certain circumstances. We benchmark the solver with quantum inputs and outputs, and characterize it by non-trace-preserving quantum process tomography, which yields a process fidelity of $0.837\pm0.006$. Our results highlight the potential of superconducting quantum circuits for applications in solving large-scale linear systems, a ubiquitous task in science and engineering.

Related articles: Most relevant | Search more
arXiv:2309.05290 [quant-ph] (Published 2023-09-11)
Solving Systems of Linear Equations: HHL from a Tensor Networks Perspective
arXiv:1302.4310 [quant-ph] (Published 2013-02-18, updated 2013-06-03)
Experimental Quantum Computing to Solve Systems of Linear Equations
X. -D. Cai et al.
arXiv:2408.12704 [quant-ph] (Published 2024-08-22)
A General Framework for Gradient-Based Optimization of Superconducting Quantum Circuits using Qubit Discovery as a Case Study