Quantum approximate optimization via learning-based adaptive optimization
This work addresses the challenge of optimizing QAOA for combinatorial problems, which is crucial for achieving quantum advantage in practical tasks, though it appears incremental as it improves an existing optimizer rather than introducing a new paradigm.
The paper tackled the problem of pervasive local minima in the Quantum Approximate Optimization Algorithm (QAOA) by designing a double adaptive-region Bayesian optimization (DARBO) method, which demonstrated superior speed, accuracy, and stability compared to conventional optimizers in numerical results and addressed measurement efficiency and quantum noise on a superconducting quantum processor.
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.