Quantum machine learning is the integration of quantum algorithms within machine learning programs.[1][2][3][4][5][6][7][8] The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning.[9][10][11] While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program.[12] This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device.[13][14][15] These routines can be more complex in nature and executed faster on a quantum computer.[7] Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data
More of the definition can be found here : https://en.wikipedia.org/wiki/Quantum_machine_learning
Why Quantum Machine Learning? Quantum Machine Learning is powerful because it can compute multiple states concurrently. When various forms are processed together, it is evident that it will exponentially reduce the time to train the machine; but for that, the data must be in the quantum form.
Classical data can only be processed by classical computing. So, to process data concurrently, quantum data is a must.
There are some reasons why we should use Quantum Machine Learning:
Since quantum data is needed for Quantum Machine Learning, its processing relies on various laws of physics known as quantum theory. Quantum Machine Learning provides aspects of quantum mechanics and vows to deliver giant leaps towards processing power. Specialists prophesy that Quantum Machine Learning will soon massively outperform even the most capable of today’s and tomorrow’s Machine Learning and Deep Learning. Machine Learning is quite a mature field now, but Quantum Machine Learning has some areas to be explored to enhance this field. Such as:
The algorithms outperform Machine Learning and Deep Learning in the accuracy measures. Today’s QML algorithms fault tolerance is very low. read more at : https://www.educative.io/answers/advantages-of-quantum-machine-learning-over-machine-learning
