Exploring the Fusion of Quantum Computing and Machine Learning

Smita Bhagwat, Pallavi Rege, Nitin Sakhare, Seema Babusing Rathod, Rupali Atul Mahajan, Archana Chougule,

In the ever-evolving landscape of machine learning, this study explores the integration of quantum computing principles into predictive modelling through the novel concept of Quantum Fusion. By leveraging the unique properties of entanglement, Quantum Fusion enhances the predictive power of machine learning ensembles. The study demonstrates significant improvements in key performance metrics, including accuracy, precision, recall, and F1 score, when compared to traditional machine learning ensembles. The introduction of Quantum Entanglement emerges as a pivotal factor in achieving these advancements. The results not only underscore the superiority of Quantum Fusion but also contribute to the growing body of research on quantum-enhanced machine learning. As quantum computing continues to advance, the implications of Quantum Fusion have the potential to redefine the capabilities of predictive modelling, opening new frontiers in solving complex problems.

Dipesh Niraula, Jamalina Jamaluddin, Julia M. Pakela +1 more

Tiffany Khou, Christopher S. Stuetzle

Vandana Milind Rohokale, Ramjee Prasad

Quantum machine learning (QML) represents the fusion of quantum computing algorithms with machine learning techniques. A prominent application of QML involves employing machine learning algorithms to analyze classical data using a quantum computer, known as quantum-enhanced machine learning. This interdisciplinary field merges concepts from quantum physics and machine learning to pioneer new algorithms and methodologies for data processing and analysis. QML seeks to harness unique quantum properties like superposition and entanglement to enhance the performance of machine learning models. It explores the synergy between quantum computing and machine learning, aiming to leverage quantum systems’ inherent characteristics for advanced data processing capabilities. This chapter provides an in-depth exploration of QML’s comprehensive approach.

Jincheng Zhang

This paper explores the integration of quantum machine learning (QML) with classic machine learning (ML) algorithms, aiming to enhance data compression and feature extraction, ultimately improving the efficiency and accuracy of machine learning models. We delve into the synergistic potential of combining these two paradigms, focusing on the unique capabilities of quantum algorithms to address limitations inherent in traditional ML. The core mechanism involves leveraging quantum machine learning techniques to achieve superior data representation and feature identification. This research investigates the practical implications of this fusion, highlighting potential benefits and challenges in the development of advanced machine learning systems.

Maria Schuld, Francesco Petruccione