Simple Quantum Circuits for Data Classification
- 308 Downloads
The paper is dedicated to the problem of supervised learning in quantum circuits. We present two solutions: SWAP-test and Simple Quantum Circuits (SQCs) based on the tree tensor networks which are able to properly classify samples from Moons, Circles, Blobs and Iris sets. Moreover, the mentioned circuits were constructed not only for qubits, but also for the units of quantum information with higher freedom level. The SWAP-test, prepared as a part of this paper, works for units like qutrits and ququads – so far this solution has been only discussed in the context of qubits. We present the procedure of data preparation which is important in further data classification with high success rate. It should be emphasized that the shown circuits are effective in pattern recognition in spite of a low level of their complexity.
KeywordsClassification Quantum circuits SWAP-test Qudits
We would like to thank for useful discussions with the Q-INFO group at the Institute of Control and Computation Engineering (ISSI) of the University of Zielona Góra, Poland. We would like also to thank to anonymous referees for useful comments on the preliminary version of this paper. The numerical results were done using the hardware and software available at the “GPU \(\mu \)-Lab” located at the Institute of Control and Computation Engineering of the University of Zielona Góra, Poland.
- 7.Murgia, M., Waters, R.: Google claims to have reached quantum supremacy. Financial Times, 20 September 2019Google Scholar
- 15.Virtanen, P., Gommers, R., Oliphant T.E.: SciPy 1.0-fundamental algorithms for scientific computing in Python. arXiv:1907.10121 (2019)
- 16.Weigang, L.: A study of parallel self-organizing map. arXiv:quant-ph/9808025v3 (1998)
- 17.Wiebe, N., Kapoor, A., Svore, K.M.: Quantum nearest-neighbor algorithms for machine learning. Quantum Inf. Comput. 15(3–4), 318–358 (2015)Google Scholar
- 18.Zhou, S.S., Loke, T., Izaac, J.A., Wang, J.B.: Quantum fourier transform in computational basis. arXiv:quant-ph/1511.04818v2 (2016)
- 19.IBM Q experience. https://quantum-computing.ibm.com/. Accessed 28 Sept 2019
- 20.Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: open source scientific tools for Python. https://www.scipy.org/. Accessed 28 Sept 2019
- 21.Rigetti QCS. https://www.rigetti.com/qcs. Accessed 28 Sept 2019