Quantum approximate optimization via learning-based adaptive optimization

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.

arXiv commentMain text: 11 pages, 4 figures, SI: 5 pages, 5 figures

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• Variational quantum computing for quantum simulation: principles, implementations, and challenges
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• Parameter Setting Heuristics Make the Quantum Approximate Optimization Algorithm Suitable for the Early Fault-Tolerant Era
• Simulating thermodynamic properties of dinuclear metal complexes using Variational Quantum Algorithms
• Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
• Universal resources for quantum approximate optimization algorithm and quantum annealing
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• End-to-End Protocol for High-Quality QAOA Parameters with Few Shots
• Generalized Probabilistic Approximate Optimization Algorithm
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• Efficient Online Quantum Circuit Learning with No Upfront Training