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Quantum Machine Learning

Quantum Machine Learning

Quantum computing applied in machine learning to boost classical machine learning algorithms.

Quantum machine learning is the utilization of quantum computing to advance and boost the classical machine learning algorithms.

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

Academic paper

Maria Schuld, Ilya Sinayskiy and Francesco Petruccione

An introduction to Quantum Machine Learning

Academic paper

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