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.
Original language | English |
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Title of host publication | ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 429-433 |
Number of pages | 5 |
ISBN (Electronic) | 9781665470131 |
DOIs | |
Publication status | Published - 2022 |
Event | 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 - Ankara, Turkey Duration: 20 Oct 2022 → 22 Oct 2022 |
Publication series
Name | ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings |
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Conference
Conference | 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 |
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Country/Territory | Turkey |
City | Ankara |
Period | 20/10/22 → 22/10/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- cost function
- open quantum system
- quantum classifier
- quantum learning
- training