Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Shuai Zeng

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.

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

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.
Paper Structure (15 sections, 12 equations, 8 figures, 1 table)

This paper contains 15 sections, 12 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Usage
  3. Web Access
  4. API Programming Interface
  5. System Design
  6. Overview
  7. Major Features and Design Details
  8. Parameter Computation Module
  9. Parameter Generation Module
  10. User Interface Module
  11. Experimental Comparison
  12. Experimental Environment and Basic Settings
  13. Experiments on the Design Rationale
  14. Experiments on System Performance
  15. Conclusion and Future Work

Figures (8)

  • Figure 1: Execution Result of the API Interface Example
  • Figure 2: Overall Architecture of Stone-in-Waiting
  • Figure 3: Parameter Computation Process
  • Figure 4: Overall Flowchart of the Parameter Generation Module (Including Parallel Processes)
  • Figure 5: Scores Obtained by Transferring Random Parameters, Matching Graph Parameters, and Original Graph Parameters
  • ...and 3 more figures