Improving Quantum Optimization to Achieve Quadratic Time Complexity

TL;DR

This paper introduces Penta-O, a novel parameter-setting strategy for QAOA that achieves quadratic time complexity and enhances performance, significantly improving the efficiency of quantum optimization algorithms.

Contribution

The paper proves a new trigonometric expression for QAOA energy expectation and introduces Penta-O, a level-wise parameter-setting method that eliminates the classical outer loop and reduces complexity.

Findings

Penta-O achieves quadratic time complexity of O(p^2).

QAOA with Penta-O attains near-optimal performance with minimal circuit depth.

The method is broadly applicable to Ising model-based quadratic unconstrained binary optimization.

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.