Local quantum annealing (LQA), an iterative algorithm, is designed to solve combinatorial optimization problems. It draws inspiration from QA, which utilizes adiabatic time evolution to determine the global minimum of a given objective function. In the original LQA, the classical Hamiltonian is minimized via gradient descent. The performance of LQA depends on the choice of the parameters. Owing to the non-convex nature of the original cost function, LQA often becomes trapped in local minima, limiting its effectiveness. To address this challenge, we combine LQA with a deep unfolding scheme, which enables us to tune the parameters from the training data via back-propagation. {As a demonstration, we apply the deep unfolded LQA to the Sherrington-Kirkpatrick model, which is a fundamental {model} in statistical physics.} Our findings exhibit that deep unfolded LQA outperforms the original LQA, exhibiting remarkable convergence speed and performance improvement. As the trained parameters can be generalized to unknown instances and different system sizes, our results have significant practical implications and provide valuable insights for real-world applications.
QAMA: Scalable Quantum Annealing Multi-Head Attention Operator for Deep Learning
School of Electronic Information, Central South University（中南大学电子信息学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Beijing QBoson Quantum Technology Co. Ltd.（北京QBoson量子科技有限公司）；Institute of Artificial Intelligence (TeleAI) of China Telecom（中国电信人工智能研究院（TeleAI））
Unfair Sampling of Quantum Annealing in Weighted Graph Bipartitioning Problems
Graduate School of Science and Technology, Keio University（庆应义塾大学理工学研究科）；Dept. of Appl. Phys. and Physico-Informatics, Graduate School of Science and Technology, KSQAIC, WPI-Bio2Q, Keio University（庆应义塾大学理工学研究科应用物理与物理信息系）
Post-Error Correction for Quantum Annealing Processor using Reinforcement Learning
Warsaw University of Technology（华沙理工大学）；Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息研究所）；Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University（雅盖隆大学物理、天文学与应用计算机科学学院）
California Institute of Technology（加州理工学院）；DeepMind Technologies（深度思维科技公司）；Lockheed Martin Advanced Technology Center（洛克希德·马丁先进技术中心）；University of Southern California（南加州大学）
An Annealing-based Quantum Neural Network with Discrete Wavelet Transforms for Enhanced Air Pollution Nowcasting
SENAI CIMATEC University（SENAI CIMATEC大学）；QuIIN - Quantum Industrial Innovation, SENAI CIMATEC University（量子工业创新中心（QuIIN），SENAI CIMATEC大学）；Latin-American Quantum Computing Center, SENAI CIMATEC University（拉丁美洲量子计算中心，SENAI CIMATEC大学）；Dynex, QEnterprises AG（Dynex公司，QEnterprises股份公司）；Surrey Institute for People-Centered Artificial Intelligence, Faculty of Engineering and Physical Sciences, University of Surrey（萨里大学工程与物理科学学院人类中心人工智能研究所）；Global Centre for Clean Research (GCARE), School of Sustainability, Civil and Environmental Engineering and Physical Sciences, University of Surrey（萨里大学可持续发展、土木与环境工程及物理科学学院全球清洁研究中心（GCARE））；Institute for Sustainability, University of Surrey（萨里大学可持续发展研究所）
