International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

Training an open quantum classifier

Ufuk Korkmaz, Melih Can Topal, Ekin Erdem Aygül, Deniz Türkpençe

Published Oct 20, 2022• 3 citations

Abstract

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.

DOI: 10.1109/ismsit56059.2022.9932705

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