• Harsh Gupta
• Binayak Dutta

With the upcoming advancement of quantum computing, the compounding of quantum mechanics and machine learning opens new doors for solving really complex problems in every domain. This research is made carefully with detailed search on Quantum Neural Network Clustering (QNNC) with a specific focus on leveraging the capabilities of quantum annealing for enhancement. Through the integration of quantum annealing, this study aims to improve the convergence speed and solution quality of QNNC. The adiabatic nature of quantum annealing is expected to provide a more efficient exploration of solution spaces, offering a potential leap forward in addressing clustering tasks. This research not only contributes to the theoretical understanding of quantum-enhanced clustering but also explores practical applications in diverse domains, including data analysis and optimization. The findings presented herein showcase the transformative potential of quantum annealing in enhancing the capabilities of Quantum Neural Network Clustering, paving the way for quantum-assisted advancements in unsupervised learning.

Quantum, Neural Network, Clustering, Machine Learning, Quantum Computing, Quantum Annealing

"Enhancing Quantum Neural Network Clustering with the help of Quantum Annealing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.a339-a349, February-2024, Available :http://www.jetir.org/papers/JETIR2402042.pdf

"Enhancing Quantum Neural Network Clustering with the help of Quantum Annealing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppa339-a349, February-2024, Available at : http://www.jetir.org/papers/JETIR2402042.pdf