1. bookVolume 30 (2020): Edition 4 (December 2020)
Détails du magazine
License
Format
Magazine
eISSN
2083-8492
Première parution
05 Apr 2007
Périodicité
4 fois par an
Langues
Anglais
Accès libre

Basic quantum circuits for classification and approximation tasks

Publié en ligne: 31 Dec 2020
Volume & Edition: Volume 30 (2020) - Edition 4 (December 2020)
Pages: 733 - 744
Reçu: 23 Jan 2020
Accepté: 29 Oct 2020
Détails du magazine
License
Format
Magazine
eISSN
2083-8492
Première parution
05 Apr 2007
Périodicité
4 fois par an
Langues
Anglais
Abstract

We discuss a quantum circuit construction designed for classification. The circuit is built of regularly placed elementary quantum gates, which implies the simplicity of the presented solution. The realization of the classification task is possible after the procedure of supervised learning which constitutes parameter optimization of Pauli gates. The process of learning can be performed by a physical quantum machine but also by simulation of quantum computation on a classical computer. The parameters of Pauli gates are selected by calculating changes in the gradient for different sets of these parameters. The proposed solution was successfully tested in binary classification and estimation of basic non-linear function values, e.g., the sine, the cosine, and the tangent. In both the cases, the circuit construction uses one or more identical unitary operations, and contains only two qubits and three quantum gates. This simplicity is a great advantage because it enables the practical implementation on quantum machines easily accessible in the nearest future.

Keywords

Augusto, L.M. (2017). Many-Valued Logics: A Mathematical and Computational Introduction, College Publications, London.Search in Google Scholar

Bertlmann, R. and Krammer, P. (2008). Bloch vectors for qudits, Journal of Physics A: Mathematical and Theoretical41(23): 235303, DOI: 10.1088/1751-8113/41/23/235303.10.1088/1751-8113/41/23/235303Search in Google Scholar

Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N. and Lloyd, S. (2017). Quantum machine learning, Nature549(7671): 195–202, DOI: 10.1038/nature23474.10.1038/nature23474Search in Google Scholar

Gibney, E. (2019). Hello quantum world! Google publishes landmark quantum supremacy claim, Nature574(7779): 461–462, DOI: 10.1038/d41586-019-03213-z.10.1038/d41586-019-03213-zSearch in Google Scholar

IBM (2019). Q Experience, https://quantum-computing.ibm.com/.Search in Google Scholar

Kołaczek, D., Spisak, B.J. and Wołoszyn, M. (2019). The phase-space approach to time evolution of quantum states in confined systems: The spectral split-operator method, International Journal of Applied Mathematics and Computer Science29(3): 439–451, DOI: 10.2478/amcs-2019-0032.10.2478/amcs-2019-0032Search in Google Scholar

Li, J., Yang, X., Peng, X. and Sun, C. (2017). Hybrid quantum-classical approach to quantum optimal control, Physical Review Letters118(15): 150503, DOI: 10.1103/PhysRevLett.118.150503.10.1103/PhysRevLett.118.150503Search in Google Scholar

Li, Z. and Li, P. (2015). Clustering algorithm of quantum self-organization network, Open Journal of Applied Sciences05(6): 270–278, DOI: 10.4236/ojapps.2015.56028.10.4236/ojapps.2015.56028Search in Google Scholar

MacMahon, D. (2007). Quantum Computing Explained, John Wiley, Hoboken, NJ.10.1002/9780470181386Search in Google Scholar

Mitarai, K., Negoro, M., Kitagawa, M. and Fujii, K. (2018). Quantum circuit learning, Physical Review Letters98(3): 032309, DOI: 10.1103/PhysRevA.98.032309.10.1103/PhysRevA.98.032309Search in Google Scholar

Narayanan, A. and Menneer, T. (2000). Quantum artificial neural network architectures and components, Information Sciences128(3–4): 231–255, DOI: 10.1016/S0020-0255(00)00055-4.10.1016/S0020-0255(00)00055-4Search in Google Scholar

Nielsen, M. and Chuang, I. (2010). Quantum Computation and Quantum Information, 10th Anniversary Edition, Cambridge University Press, Cambridge.10.1017/CBO9780511976667Search in Google Scholar

Ortigoso, J. (2018). Twelve years before the quantum no-cloning theorem, American Journal of Physics86(3): 201–205, DOI: 10.1119/1.5021356.10.1119/1.5021356Search in Google Scholar

Park, J. (1970). The concept of transition in quantum mechanics, Foundations of Physics1(1): 23–33, DOI: 10.1007/BF00708652.10.1007/BF00708652Search in Google Scholar

Pati, A.K. and Braunstein, S.L. (2000). Impossibility of deleting an unknown quantum state, Nature404(6774): 164–165, DOI: 10.1038/35004532.10.1038/3500453210724163Search in Google Scholar

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python, Journal of Machine Learning Research12: 2825–2830, DOI: 10.5555/1953048.2078195.Search in Google Scholar

Pérez-Salinas, A., Cervera-Lierta, A., Gil-Fuster, E. and Latorre, J. (2020). Data re-uploading for a universal quantum classifier, Quantum4: 226, DOI: 10.22331/q-2020-02-06-226.10.22331/q-2020-02-06-226Search in Google Scholar

Rigetti (2019). Quantum Computing Systems, https://www.rigetti.com/systems.Search in Google Scholar

Schuld, M., Sinayskiy, I. and Petruccione, F. (2014). Quantum computing for pattern classification, in D.-N. Pham and S.-B. Park (Eds), PRICAI 2014: Trends in Artificial Intelligence, Springer, Cham, pp. 208–220, DOI: 10.1007/978-3-319-13560-1_17.10.1007/978-3-319-13560-1_17Search in Google Scholar

Schuld, M., Sinayskiy, I. and Petruccione, F. (2015). An introduction to quantum machine learning, Contemporary Physics56(2): 172–185, DOI: 10.1080/00107514.2014.964942.10.1080/00107514.2014.964942Search in Google Scholar

Veenman, C. and Reinders, M. (2005). The nearest sub-class classifier: a compromise between the nearest mean and nearest neighbor classifier, IEEE Transactions on Pattern Analysis and Machine Intelligence27(9): 1417–1429, DOI: 10.1109/TPAMI.2005.187.10.1109/TPAMI.2005.18716173185Search in Google Scholar

Weigang, L. (1998). A study of parallel self-organizing map, arXiv: quant-ph/9808025v3.Search in Google Scholar

Wiebe, N., Kapoor, A. and Svore, M. (2015). Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning, Quantum Information and Computation15(3–4): 316–356.10.26421/QIC15.3-4-7Search in Google Scholar

Wiśniewska, J. and Sawerwain, M. (2020). Simple quantum circuits for data classification, in N.T. Nguyen et al. (Eds), Intelligent Information and Database Systems, Springer, Cham, pp. 392–403, DOI: 0.1007/978-3-030-41964-6_34.Search in Google Scholar

Wootters, W. and Zurek, W. (1982). A single quantum cannot be cloned, Nature299(5886): 802–803, DOI: 10.1038/299802a0.10.1038/299802a0Search in Google Scholar

Zoufal, C., Lucchi, A. and Woerner, S. (2019). Quantum generative adversarial networks for learning and loading random distributions, Quantum Information5(1): 103, DOI: 10.1038/s41534-019-0223-2.10.1038/s41534-019-0223-2Search in Google Scholar

Articles recommandés par Trend MD

Planifiez votre conférence à distance avec Sciendo