An Investigation of Quantum Deep Clustering Framework with Quantum Deep SVM & Convolutional Neural Network Feature Extractor

TL;DR

This paper introduces a novel quantum deep clustering framework combining quantum deep SVM, CNN feature extraction, and quantum K-Means, demonstrating significant computational speedups over classical methods.

Contribution

It presents a new quantum deep clustering approach integrating quantum SVM, CNN features, and quantum K-Means, with analysis of its computational advantages.

Findings

Significant exponential speedup over classical clustering methods.

Demonstration of a novel quantum-classical hybrid clustering framework.

Potential for enhanced performance in quantum machine learning applications.

Abstract

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.