Quantum Machine Learning (QML) is a new field at the intersection of quantum computing and machine learning. It combines the potential of quantum computing to handle complex problems, with the data-driven techniques of machine learning, allowing for creative solutions to difficult problems.
QML offers a unique approach to machine learning by exploiting principles from quantum physics such as superposition, entanglement, and tunneling. It is related to conventional machine learning approaches, but optimized to provide increased performance and scalability for certain tasks.
QML algorithms leverage the ability of quantum systems to represent data in multiple states simultaneously, making them particularly well suited to tasks such as search, optimization, and modeling of complex data patterns. It is expected to play an important role in areas such as artificial intelligence and automated decision-making.
QML can also address issues such as generalization and conformation to changing data. Additionally, successful completion of large scale problems can be achieved in a matter of minutes or hours. Typically, certain problems that are impossible with classical approaches, such as quantum chemistry simulations and financial analysis, can be solved with the help of QML.
There is an increasing amount of research being conducted in the field of QML, with the potential for a number of applications in both the scientific and commercial realms. It is expected that QML will be in the forefront of problem solving and research in the years to come.
