Max-cut Clustering Utilizing Warm-Start QAOA and IBM Runtime

2021/08/30 by Daniel Beaulieu, Beaulieu, Daniel, Anh Hoang Pham +1 · 1 citation
Computer Science · #Advanced Clustering Algorithms Research #FOS: Physical sciences #Face and Expression Recognition #Neural Networks and Applications #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2108.13464

openalex publication_date 2021/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Quantum optimization algorithms can be used to recreate unsupervised learning clustering of data by mapping the problem to a graph optimization problem and finding the minimum energy for a MaxCut problem formulation. This research tests the "Warm Start" variant of Quantum Approximate Optimization Algorithm (QAOA) versus the standard implementation of QAOA for unstructured clustering problems. The performance for IBM's new Qiskit Runtime API for speeding up optimization algorithms is also tested in terms of speed up and relative performance compared to the standard implementation of optimization algorithms. Warm-start QAOA performs better than any other optimization algorithm, though standard QAOA runs the fastest. This research also used a non-convex optimizer to relax the quadratic program for the Warm-start QAOA.

Citations

• Unsupervised Machine Learning on a Hybrid Quantum Computer
• Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
• K-Means Clustering on Noisy Intermediate Scale Quantum Computers
• VQE Method: A Short Survey and Recent Developments

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