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Simple Quantum Circuits for Data Classification

  • Joanna WiśniewskaEmail author
  • Marek Sawerwain
Conference paper
  • 308 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)

Abstract

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.

Keywords

Classification Quantum circuits SWAP-test Qudits 

Notes

Acknowledgments

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.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Information Systems, Faculty of CyberneticsMilitary University of TechnologyWarsawPoland
  2. 2.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

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