Mitigating Barren Plateaus with Transfer-learning-inspired Parameter Initializations

• URL: http://arxiv.org/abs/2112.10952v3
• Date: Mon, 6 Feb 2023 00:32:27 GMT
• Title: Mitigating Barren Plateaus with Transfer-learning-inspired Parameter Initializations
• Authors: Huan-Yu Liu, Tai-Ping Sun, Yu-Chun Wu, Yong-Jian Han, and Guo-Ping Guo
• Abstract summary: Variational quantum algorithms (VQAs) are widely applied in the noisy intermediate-scale quantum era. Training VQAs faces difficulties, one of which is the so-called barren plateaus (BP) phenomenon.
• Score: 2.4290469931265344
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Variational quantum algorithms (VQAs) are widely applied in the noisy intermediate-scale quantum era and are expected to demonstrate quantum advantage. However, training VQAs faces difficulties, one of which is the so-called barren plateaus (BP) phenomenon, where gradients of cost functions vanish exponentially with the number of qubits. In this paper, inspired by transfer learning, where knowledge of pre-solved tasks could be further used in a different but related work with training efficiency improved, we report a parameter initialization method to mitigate BP. In the method, a small-sized task is solved with a VQA. Then the ansatz and its optimum parameters are transferred to tasks with larger sizes. Numerical simulations show that this method could mitigate BP and improve training efficiency. A brief discussion on how this method can work well is also provided. This work provides a reference for mitigating BP, and therefore, VQAs could be applied to more practical problems.

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