@misc {pub4013,
	title = {Machine Learning on Near-Term Universal Quantum Computers},
	author = {Manuel Rudolph AND Sebastian Schmitt AND Fred Jendrzejewski},
	year = {2019},
	abstract = {Implementing near-term quantum computers with a small number of qubits and imperfect gate fidelities
for real world challenges has been a flourishing field of research in recent years. Quantum-classical hybrid
algorithms with shallow quantum circuits for state preparation are being used with success in fields like
quantum chemistry and machine learning. This work focuses on the use of near-term quantum computers
for unsupervised machine learning on classical data sets with different model infrastructures. It is shown
that the quantum state is able to learn the statistics and correlations of data using shallow variational state
preparation. Simple data sets are used to study general aspects such as learning, sampling and generalization
of such quantum machine learning implementations in search of practical applications for small quantum
machines.},
	publisher = {Deutsche Physikalische Gesellschaft},
	booktitle = {1st DPG Fall Meeting - Quantum Science and Information Technologies}
}