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- W4309226443 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." @default.
- W4309226443 created "2022-11-24" @default.
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- W4309226443 date "2022-10-20" @default.
- W4309226443 modified "2023-09-27" @default.
- W4309226443 title "Training an open quantum classifier" @default.
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- W4309226443 doi "https://doi.org/10.1109/ismsit56059.2022.9932705" @default.
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