  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.
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.
