Coherence and entanglement dynamics in training variational quantum perceptronopen access
- Authors
- Namkung, M.; Kwon, Y.
- Issue Date
- Nov-2020
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Keywords
- Coherence; Coherence depletion; Coherence distribution; Entanglement; Quantum computer; Quantum machine learning; Quantum supremacy
- Citation
- Entropy, v.22, no.11, pp 1 - 14
- Pages
- 14
- Indexed
- SCIE
SCOPUS
- Journal Title
- Entropy
- Volume
- 22
- Number
- 11
- Start Page
- 1
- End Page
- 14
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1839
- DOI
- 10.3390/e22111277
- ISSN
- 1099-4300
1099-4300
- Abstract
- In quantum computation, what contributes supremacy of quantum computation? One of the candidates is known to be a quantum coherence because it is a resource used in the various quantum algorithms. We reveal that quantum coherence contributes to the training of variational quantum perceptron proposed by Y. Du et al., arXiv:1809.06056 (2018). In detail, we show that in the first part of the training of the variational quantum perceptron, the quantum coherence of the total system is concentrated in the index register and in the second part, the Grover algorithm consumes the quantum coherence in the index register. This implies that the quantum coherence distribution and the quantum coherence depletion are required in the training of variational quantum perceptron. In addition, we investigate the behavior of entanglement during the training of variational quantum perceptron. We show that the bipartite concurrence between feature and index register decreases since Grover operation is only performed on the index register. Also, we reveal that the concurrence between the two qubits of index register increases as the variational quantum perceptron is trained. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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