Variational Quantum and Quantum-Inspired Clustering
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arXiv
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866910287509061632 |
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| author | Bermejo, Pablo Orus, Roman |
| author_facet | Bermejo, Pablo Orus, Roman |
| contents | Here we present a quantum algorithm for clustering data based on a variational quantum circuit. The algorithm allows to classify data into many clusters, and can easily be implemented in few-qubit Noisy Intermediate-Scale Quantum (NISQ) devices. The idea of the algorithm relies on reducing the clustering problem to an optimization, and then solving it via a Variational Quantum Eigensolver (VQE) combined with non-orthogonal qubit states. In practice, the method uses maximally-orthogonal states of the target Hilbert space instead of the usual computational basis, allowing for a large number of clusters to be considered even with few qubits. We benchmark the algorithm with numerical simulations using real datasets, showing excellent performance even with one single qubit. Moreover, a tensor network simulation of the algorithm implements, by construction, a quantum-inspired clustering algorithm that can run on current classical hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_09893 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Variational Quantum and Quantum-Inspired Clustering Bermejo, Pablo Orus, Roman Quantum Physics Artificial Intelligence Here we present a quantum algorithm for clustering data based on a variational quantum circuit. The algorithm allows to classify data into many clusters, and can easily be implemented in few-qubit Noisy Intermediate-Scale Quantum (NISQ) devices. The idea of the algorithm relies on reducing the clustering problem to an optimization, and then solving it via a Variational Quantum Eigensolver (VQE) combined with non-orthogonal qubit states. In practice, the method uses maximally-orthogonal states of the target Hilbert space instead of the usual computational basis, allowing for a large number of clusters to be considered even with few qubits. We benchmark the algorithm with numerical simulations using real datasets, showing excellent performance even with one single qubit. Moreover, a tensor network simulation of the algorithm implements, by construction, a quantum-inspired clustering algorithm that can run on current classical hardware. |
| title | Variational Quantum and Quantum-Inspired Clustering |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2206.09893 |