Quantum approximate optimization via learning-based adaptive optimization

• URL: http://arxiv.org/abs/2303.14877v3
• Date: Sat, 9 Mar 2024 20:01:31 GMT
• Title: Quantum approximate optimization via learning-based adaptive optimization
• Authors: Lixue Cheng, Yu-Qin Chen, Shi-Xin Zhang, Shengyu Zhang
• Abstract summary: Quantum approximate optimization algorithm (QAOA) is designed to solve objective optimization problems. Our results demonstrate that the algorithm greatly outperforms conventional approximations in terms of speed, accuracy, efficiency and stability. This work helps to unlock the full power of QAOA and paves the way toward achieving quantum advantage in practical classical tasks.
• Score: 5.399532145408153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve combinatorial optimization problems by transforming the discrete optimization problem into a classical optimization problem over continuous circuit parameters. QAOA objective landscape is notorious for pervasive local minima, and its viability significantly relies on the efficacy of the classical optimizer. In this work, we design double adaptive-region Bayesian optimization (DARBO) for QAOA. Our numerical results demonstrate that the algorithm greatly outperforms conventional optimizers in terms of speed, accuracy, and stability. We also address the issues of measurement efficiency and the suppression of quantum noise by conducting the full optimization loop on a superconducting quantum processor as a proof of concept. This work helps to unlock the full power of QAOA and paves the way toward achieving quantum advantage in practical classical tasks.

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