1. Quantum state clustering algorithm based on variational quantum circuit.
- Author
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Fang, Pengpeng, Zhang, Cai, and Situ, Haozhen
- Subjects
- *
QUANTUM states , *ALGORITHMS , *MACHINE learning , *LEARNING communities - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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