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- Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
- Quantum Information Processing
- 2014

- Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
- PRICAI
- 2014

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning research using the example of… (More)

- Maria Schuld, Francesco Petruccione
- ArXiv
- 2017

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer.… (More)

- Maria Schuld, Francesco Petruccione
- Encyclopedia of Machine Learning and Data Mining
- 2017

- Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
- ArXiv
- 2014

Perceptrons are the basic computational unit of artificial neural networks, as they model the activation mechanism of an output neuron due to incoming signals from its neighbours. As linear classifiers, they play an important role in the foundations of machine learning. In the context of the emerging field of quantum machine learning, several attempts have… (More)

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