TY - GEN
T1 - A Comparative Analysis of Hybrid-Quantum Classical Neural Networks
AU - Zaman, Kamila
AU - Ahmed, Tasnim
AU - Hanif, Muhammad Abdullah
AU - Marchisio, Alberto
AU - Shafique, Muhammad
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Hybrid Quantum-Classical Machine Learning (ML) is an emerging field, amalgamating the strengths of both classical neural networks and quantum variational circuits on the current noisy intermediate-scale quantum devices [1]. This paper performs an extensive comparative analysis between different hybrid quantum-classical machine learning algorithms, namely Quantum Convolution Neural Network, Quanvolutional Neural Network and Quantum ResNet, for image classification. The experiments designed in this paper focus on different Quantum ML (QML) algorithms to better understand the accuracy variation across the different quantum architectures by implementing interchangeable quantum circuit layers, varying the repetition of such layers and their efficient placement. Such variations enable us to compare the accuracy across different architectural permutations of a given hybrid QML algorithm. The performance comparison of the hybrid models, based on the accuracy, provides us with an understanding of hybrid quantum-classical convergence in correlation with the quantum layer count and the qubit count variations in the circuit.
AB - Hybrid Quantum-Classical Machine Learning (ML) is an emerging field, amalgamating the strengths of both classical neural networks and quantum variational circuits on the current noisy intermediate-scale quantum devices [1]. This paper performs an extensive comparative analysis between different hybrid quantum-classical machine learning algorithms, namely Quantum Convolution Neural Network, Quanvolutional Neural Network and Quantum ResNet, for image classification. The experiments designed in this paper focus on different Quantum ML (QML) algorithms to better understand the accuracy variation across the different quantum architectures by implementing interchangeable quantum circuit layers, varying the repetition of such layers and their efficient placement. Such variations enable us to compare the accuracy across different architectural permutations of a given hybrid QML algorithm. The performance comparison of the hybrid models, based on the accuracy, provides us with an understanding of hybrid quantum-classical convergence in correlation with the quantum layer count and the qubit count variations in the circuit.
KW - Hybrid Quantum-Classical Neural Networks
KW - Quantum Convolutional Neural Networks
KW - Quantum Machine Learning
KW - Quantum ResNet
KW - Quanvolutional Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=105002372531&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-85884-0_9
DO - 10.1007/978-3-031-85884-0_9
M3 - Conference contribution
AN - SCOPUS:105002372531
SN - 9783031858833
T3 - Communications in Computer and Information Science
SP - 102
EP - 115
BT - Grid, Cloud, and Cluster Computing; Quantum Technologies; and Modeling, Simulation and Visualization Methods - 20th International Conference, GCC 2024, 3rd International Conference, ICEQT 2024, and 21st International Conference, MSV 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024, Revised Selected Papers
A2 - Arabnia, Hamid R.
A2 - Takata, Masami
A2 - Deligiannidis, Leonidas
A2 - Rivas, Pablo
A2 - Ohue, Masahito
A2 - Yasuo, Nobuaki
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd International Conference on Quantum Technologies, ICEQT 2024, and 21st International Conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024
Y2 - 22 July 2024 through 25 July 2024
ER -