Analyzing the effect of feature mapping techniques along with the circuit depth in quantum supervised learning by utilizing quantum support vector machine-Reference-Cited by-同舟云学术

Analyzing the effect of feature mapping techniques along with the circuit depth in quantum supervised learning by utilizing quantum support vector machine

Published:2021-12-18 Issue: Volume: Page:
ISSN:
Container-title:2021 24th International Conference on Computer and Information Technology (ICCIT)
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Author:

Hossain Muhammad Minoar¹,Ali Mohammed Sowket²,Swarna Reshma Ahmed¹,Hasan Md. Mahmodul¹,Habib Nahida³,Rahman Md. Wahidur⁴,Azad Mir Mohammad⁴,Rahman Mohammad Motiur¹

Affiliation:

1. Mawlana Bhashani Science and Technology University,Computer Science and Engineering,Tangail,Bangladesh

2. Bangladesh Army University of Science and Technology,Computer Science and Engineering,Nilphamari,Bangladesh

3. Ranada Prasad Shaha University,Computer Science and Engineering,Narayanganj,Bangladesh

4. Khwaja Yunus Ali University,Computer Science and Engineering,Shirajgonj,Bangladesh

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9689773/9689775/09689853.pdf?arnumber=9689853

Reference25 articles.

1. Quantum Support Vector Machine for Big Data Classification

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2. A Quantum Machine Learning Methodology for Precise Appliance Identification in Smart Grids;2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA);2023-07-10

3. Hybrid Classical-Quantum Neural Network for Improving Space Weather Detection and Early Warning Alerts;2023 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW);2023-06-20

4. DEQSVC: Dimensionality Reduction and Encoding Technique for Quantum Support Vector Classifier Approach to Detect DDoS Attacks;IEEE Access;2023