Training an open quantum classifier

Korkmaz U., Topal M. C., Aygul E., Türkpençe D.

6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022, Ankara, Turkey, 20 - 22 October 2022, pp.429-433 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ismsit56059.2022.9932705
  • City: Ankara
  • Country: Turkey
  • Page Numbers: pp.429-433
  • Keywords: cost function, open quantum system, quantum classifier, quantum learning, training
  • Istanbul Technical University Affiliated: Yes

Abstract

© 2022 IEEE.Quantum machine learning (QML) aims to embed the power of quantum computation with learning theory. Quan-Tum noise and finding the best recipe for encoding classical information into a quantum register could be seen as challenges to overcome for computational performance. Classification of quantum information is a subtask for QML. In this study, we adopt a dissipative route for quantum data classification and examine the developed theory on a gradient descent-based learning task. In particular, we follow repeated interactions based on open quantum dynamics where the binary decision is encoded on a steady state. Based on the analytical results, we develop a cost function for training an open quantum neuron. We demonstrate that the dissipation-driven protocol is suitable for a supervised learning scheme.