Quantum algorithms are developed to solve typical problems of machine learning using the efficiency of quantum computing. Quantum machine learning is executed by adapting the classical machine learning algorithms to run on quantum computers.
It is a field aiming to advance classical machine learning. Machine learning algorithms gain a desired input-output relation from patterns and examples to interpret new inputs. This is vital for tasks such as image and speech recognition or strategy optimization, with expanding applications in the IT industry. In the last couple of years, researchers studied if quantum computing can help to improve classical machine learning algorithms. Different ideas were raised, from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory.
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Hoi-Kwan Lau, Raphael Pooser, George Siopsis, and Christian Weedbrook
Quantum Machine Learning over Infinite Dimensions
Maria Schuld, Ilya Sinayskiy and Francesco Petruccione
An introduction to Quantum Machine Learning
Quantum Machine Learning What Quantum Computing Means to Data Mining
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- Machine learningA field of computer science enabling computers to learn.
- Quantum computingThe study of computation systems that make use of quantum mechanical phenomena (eg entanglement and superposition). Quantum computers use qubits (rather than binary 0 and 1s) which can be in both states at the same time by being in a superposition of states.