2019/09/21 by Arit Kumar Bishwas, Bishwas, Arit Kumar, Ashish Mani +3
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Reservoir Computing #Quantum Computing Algorithms and Architecture
paper · pdf · doi:10.48550/arxiv.1909.09852
openalex publication_date 2019/09/21 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
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.