1. Improving Quantum Optimization to Achieve Quadratic Time Complexity
- Author
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Jiang, Ji, Huang, Peisheng, Wu, Zhiyi, Sun, Xuandong, Guo, Zechen, Huang, Wenhui, Zhang, Libo, Zhou, Yuxuan, Zhang, Jiawei, Guo, Weijie, Linpeng, Xiayu, Liu, Song, Ren, Wenhui, Tao, Ziyu, Chu, Ji, Niu, Jingjing, Zhong, Youpeng, and Yu, Dapeng
- Subjects
Quantum Physics - Abstract
Quantum Approximate Optimization Algorithm (QAOA) is a promising candidate for achieving quantum advantage in combinatorial optimization. However, its variational framework presents a long-standing challenge in selecting circuit parameters. In this work, we prove that the energy expectation produced by QAOA can be expressed as a trigonometric function of the final-level mixer parameter. Leveraging this insight, we introduce Penta-O, a level-wise parameter-setting strategy that eliminates the classical outer loop, maintains minimal sampling overhead, and ensures non-decreasing performance. This method is broadly applicable to the generic quadratic unconstrained binary optimization formulated as the Ising model. For a $p$-level QAOA, Penta-O achieves an unprecedented quadratic time complexity of $\mathcal{O}(p^2)$ and a sampling overhead proportional to $5p+1$. Through experiments and simulations, we demonstrate that QAOA enhanced by Penta-O achieves near-optimal performance with exceptional circuit depth efficiency. Our work provides a versatile tool for advancing variational quantum algorithms., Comment: 13 pages, 4 figures
- Published
- 2025