I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
Quantum machine learning is the integration of quantum algorithms within machine learning programs.
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. Quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage algorithms in a program.
In quantum computing, a qubit or quantum bit is a basic unit of quantum information - the quantum version of the classical binary bit physically realized with a two-state device. A qubit is a two-state quantum-mechanical system, one of the simplest quantum systems displaying the peculiarity of quantum mechanics.
This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex in nature and executed faster on a quantum computer.
Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving and often expediting classical machine learning techniques. Such algorithms typically require one to encode the given classical data set into a quantum computer to make it accessible for quantum information processing. Subsequently, quantum information processing routines are applied and the result of the quantum computation is read out by measuring the quantum system.
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