Stone-in-Waiting: A Cloud-Based Accelerator for the Quantum Approximate Optimization Algorithm

TL;DR

Stone-in-Waiting is a cloud-based system that enhances QAOA initialization using novel algorithms, significantly improving optimization scores and providing accessible interfaces for quantum algorithm experimentation.

Contribution

The paper introduces a new cloud accelerator with four innovative algorithms for QAOA parameter initialization, improving performance and usability.

Findings

Parameter scores improved by 40.19% over baseline

Four algorithms compared for effectiveness and efficiency

System validated through experimental results

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) and its advanced variant, the Quantum Alternating Operator Ansatz (QAOA), are major research topics in the current era of Noisy Intermediate-Scale Quantum (NISQ) computing. However, the problem of initializing their parameters remains unresolved. Motivated by the combinatorial optimization task in the 6th MindSpore Quantum Computing Hackathon (2024), this paper proposes Stone-in-Waiting, a cloud-based accelerator for obtaining high-quality initial parameters for QAOA. Internally, the accelerator builds on state-of-the-art theories and methods for parameter determination and integrates four self-developed algorithms for QAOA parameter initialization, mainly based on Bayesian methods, nearest-neighbor methods, and metric learning. Compared with the Baseline Algorithm, the generated parameters improve the score by 40.19%. Externally, the accelerator offers both a web interface and an API, providing flexible and convenient access for users to test and develop related experiments and applications. This paper presents the design principles and methods of Stone-in-Waiting, demonstrates its functional characteristics, compares the strengths and weaknesses of the four proposed algorithms, and validates the overall system performance through experiments.