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Open Access

Optimization of Quantum Computing Models Inspired by D-Wave Quantum Annealing

Baonan Wang, Feng Hu, Chao Wang( )
Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
State Key Laboratory of Cryptology, Beijing 100878, China.
Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen 518000, China.
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Abstract

With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important. The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations. This study constructs a new Quantum-Inspired Annealing (QIA) framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one. Through various configurations of the 1D Ising model, the new framework can achieve ground state, corresponding to the optimum of classical problems, with higher probability up to 28% versus classical counterpart (22% in case). This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian, but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.

Keywords

Quantum Annealing (QA)annealing schedulequantum tunnelingoptimization problem

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Tsinghua Science and Technology
Volume 25 Issue 4,
August 2020
Pages 508-515
DOI: 10.26599/TST.2019.9010030
Cite this article:
Wang B, Hu F, Wang C. Optimization of Quantum Computing Models Inspired by D-Wave Quantum Annealing. Tsinghua Science and Technology, 2020, 25(4): 508-515. https://doi.org/10.26599/TST.2019.9010030

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Received: 27 June 2019
Revised: 27 June 2019
Accepted: 17 July 2019
Published: 13 January 2020
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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