Quantum Machine Learning

Quantum Machine Learning (QML) is a multidisciplinary field that attempts to leverage the fundamental properties of the quantum world to empower conventional machine learning algorithms to handle scalability challenges. Strange behaviour of quantum particles such as superposition, entanglement and teleportation enable QML to find needles in the haystack in a far more efficient way than classical machine learning on conventional computers can. This was amply demonstrated in 1994 by Peter Shor and in 1995 by Lov Grover through their respective quantum algorithms. This field, even with all the technical challenges of quantum computing, has the potential to make non-tractable problems into tractable ones.