QUANTUM COMPUTING AND QUANTUM MACHINE LEARNING
Ashok Kumar, Manoj Kumar Gupta, Shyam Lal
Quantum machine learning lies at the intersection of quantum computing and classical machine learning, two rapidly advancing research fields. As the data required for classical models continues to grow, quantum computing offers a solution by handling large datasets more efficiently and potentially accelerating learning algorithms. Unlike classical systems, quantum systems generate unique patterns, suggesting that quantum computers may surpass classical ones in machine learning tasks. This paper reviews the current literature on quantum machine learning and its potential advantages in run time, capacity, and learning efficiency. Examples include quantum-enhanced models such as the hybrid Helmholtz machine, which improves run time through quantum sampling, and quantum neural networks, which offer increased capacity and better learning efficiency.
Iterative International Publishers eBooks•Jun 5, 2025•1 citation