QML
Quantum Machine Learning
Quantum Machine Learning
Integration of quantum algorithms within ML models to improve computational speed and data handling abilities.
Quantum Machine Learning refers to the application of quantum computing principles to machine learning models. Leveraging the fundamental properties of quantum mechanics, such as superposition and entanglement, this approach improves the processing power and computation speed of machine learning algorithms. As a result, it can handle vast amounts of data, untangle complex models, and solve problems that are currently infeasible with classical computers. While Challenges such as quantum hardware limitations and algorithmic complexity exist, the rise of Quantum Machine Learning offers potential breakthroughs in fields like drug discovery, cybersecurity, financial modeling, and climate forecasting.
The emergence of quantum computing led to developments in quantum machine learning in early 2000's, but the term gained mainstream recognition around 2014 due to increasing interest in quantum computing's potential to revolutionize AI.
Dr. Seth Lloyd, a professor of mechanical engineering and physics at MIT, is a key researcher in the field of quantum computing and its application to machine learning. Additionally, companies like IBM, Google, and Microsoft have invested heavily in quantum computing research, contributing to the evolution of Quantum Machine Learning.