Category: Quantum Machine Learning

Quantum Machine Learning (QML) is an interdisciplinary field that bridges the gap between quantum physics and machine learning (ML). It leverages principles of quantum mechanics and quantum computing to enhance traditional machine learning algorithms or create novel algorithms altogether. The key concepts in QML include quantum states, superposition, entanglement, and quantum gates, which collectively contribute to quantum speedup. This speedup arises from the inherent parallelism of quantum systems, allowing computations to be performed more quickly than on classical systems. Additionally, QML also employs quantum versions of familiar ML components such as quantum neural networks, quantum support vector machines, and quantum principal component analysis. The use of quantum systems for data processing and modeling is expected to revolutionize the field of machine learning, providing new ways to solve complex problems, perform computations, and analyze large datasets.