An Investigation of Quantum Deep Clustering Framework with Quantum Deep SVM & Convolutional Neural Network Feature Extractor
This work addresses computational bottlenecks in machine learning for researchers in quantum computing, though it appears incremental as it builds on existing quantum-classical hybrid methods.
The authors tackled the problem of improving computational efficiency in deep clustering by proposing a quantum deep clustering framework that combines quantum deep SVM, convolutional neural networks, and quantum K-Means clustering, achieving exponential speed-up gains compared to classical implementations.
In this paper, we have proposed a deep quantum SVM formulation, and further demonstrated a quantum-clustering framework based on the quantum deep SVM formulation, deep convolutional neural networks, and quantum K-Means clustering. We have investigated the run time computational complexity of the proposed quantum deep clustering framework and compared with the possible classical implementation. Our investigation shows that the proposed quantum version of deep clustering formulation demonstrates a significant performance gain (exponential speed up gains in many sections) against the possible classical implementation. The proposed theoretical quantum deep clustering framework is also interesting & novel research towards the quantum-classical machine learning formulation to articulate the maximum performance.