Quantum state clustering algorithm based on variational quantum circuit

Clustering, a well-studied problem in the machine learning community, becomes even more intriguing with the emergence of quantum machine learning. Specifically, exploring clustering techniques for quantum data, such as quantum states, holds great interest. This paper introduces a quantum state clust...

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Bibliographic Details
Published in:Quantum information processing 2024-03, Vol.23 (4), Article 125
Main Authors: Fang, Pengpeng, Zhang, Cai, Situ, Haozhen
Format: Article
Language:English
Subjects:
Data Structures and Information Theory
Mathematical Physics
Physics
Physics and Astronomy
Quantum Computing
Quantum Information Technology
Quantum Physics
Spintronics
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Summary:Clustering, a well-studied problem in the machine learning community, becomes even more intriguing with the emergence of quantum machine learning. Specifically, exploring clustering techniques for quantum data, such as quantum states, holds great interest. This paper introduces a quantum state clustering algorithm that utilizes variational quantum circuits. Our algorithm transforms the clustering problem into a parameter optimization task involving parametric quantum circuits. Each cluster is represented by a variational quantum circuit (VQC), which learns to extract the distinctive feature of its corresponding cluster during the optimization process. To guide the optimization of circuit parameters, we design an objective function that encourages each cluster’s feature extractor to produce features similar to states within its own cluster and dissimilar to states in other clusters. We construct four quantum state datasets for testing the effectiveness of our algorithm. The numerical results demonstrate that our algorithm can achieve satisfying performance.
ISSN:1573-1332
1573-1332
DOI:10.1007/s11128-024-04338-5