Efficient depth selection for the implementation of noisy quantum approximate optimization algorithm |
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| Institution: | 1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, P. R. China;2. College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, P. R. China;3. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China;4. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region of China, P. R. China The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, Guang Dong 518057, P. R. China;1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, 266520, China;2. School of Mathematical Sciences, Liaocheng University, Liaocheng, Shandong, 252059, China;3. School of Automation, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China;1. Electrical Engineering Department, School of Electrical Engineering and Information Technology, German-Jordanian University, Amman 11180, Jordan;2. Department of Electrical Engineering, The University of Jordan, Amman 11942, Jordan;1. College of Engineering University of Hail Po.Box 2440, Hail, Kingdom of Saudi Arabia;2. National School of Engineering of Sfax, University of Sfax, Lab-STA, LR11ES50, 3038, Sfax, Tunisia;3. Modeling, Information, and Systems Laboratory, University of Picardie Jules Verne, UFR of Sciences, 33 Rue St Leu Amiens 80000, France |
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| Abstract: | Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with control depth. In principle, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the regularized model selection algorithm to identify the optimal depth with just a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises. |
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