Training an open quantum classifier

Ufuk Korkmaz; Melih Can Topal; Ekin Aygul; Deniz Turkpence

doi:10.1109/ISMSIT56059.2022.9932705

Training an open quantum classifier

Ufuk Korkmaz
, Melih Can Topal
, Ekin Aygul
, Deniz Turkpence

Department of Electrical Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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
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	https://doi.org/10.1109/ISMSIT56059.2022.9932705
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

Conference

Conference	6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022
Country/Territory	Turkey
City	Ankara
Period	20/10/22 → 22/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

The authors acknowledge support from the Scientific and Technological Research Council of Turkey (TÜB˙TAK-Grant No. 120F353). The authors also wish to extend special thanks to the Cognitive Systems Lab in the Department of Electrical Engineering providing the atmosphere for motivational and stimulating discussions.

Funders	Funder number
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu	120F353

Keywords

cost function
open quantum system
quantum classifier
quantum learning
training

Access to Document

10.1109/ISMSIT56059.2022.9932705

Cite this

Korkmaz, U., Topal, M. C., Aygul, E., & Turkpence, D. (2022). Training an open quantum classifier. In ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings (pp. 429-433). (ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISMSIT56059.2022.9932705

Korkmaz, Ufuk ; Topal, Melih Can ; Aygul, Ekin et al. / Training an open quantum classifier. ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 429-433 (ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings).

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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.",

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Korkmaz, U, Topal, MC, Aygul, E & Turkpence, D 2022, Training an open quantum classifier. in ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings. ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 429-433, 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022, Ankara, Turkey, 20/10/22. https://doi.org/10.1109/ISMSIT56059.2022.9932705

Training an open quantum classifier. / Korkmaz, Ufuk; Topal, Melih Can; Aygul, Ekin et al.
ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 429-433 (ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Turkpence, Deniz

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N2 - 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.

AB - 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.

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Korkmaz U, Topal MC, Aygul E, Turkpence D. Training an open quantum classifier. In ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 429-433. (ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings). doi: 10.1109/ISMSIT56059.2022.9932705

Training an open quantum classifier

Abstract

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Bibliographical note

Funding

Keywords

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