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The aim of this work is to explore practical implementations of Quantum and Quantum-assisted Machine Learning algorithms and benchmark potential bene- ts of utilizing quantum phenomena in Quantum-assisted Neural Networks with qubit layers. Two known approaches of generative Quantum Machine Learning algorithms are revised to demonstrate the encoding capability and sampling benets of qubits. As one possible extension of those generative models, the Quantum-assisted Generator (QaG) is presented which implements a qubit layer coupled to subsequent classical layers. The QaG provides promising indications that quantum phenomena may enhance performance of a generative neural network. This work also considers Hamiltonian-based and gate-based implementations of quantum-assisted Autoencoders. Using an eective hybrid training approach consisting of simultaneous application of conventional backpropagation and blackbox optimization, we show that the Hamiltonian-based Autoencoder is able to learn the MNIST data set with good generalization properties. Several approaches to extend the presented quantum-assisted algorithms are proposed to further investigate potential benets of utilizing quantum systems in combination with Articial Neural Networks.

The aim of this work is to explore practical implementations of Quantum and Quantum-assisted Machine Learning algorithms and benchmark potential bene- ts of utilizing quantum phenomena in Quantum-assisted Neural Networks with qubit layers. Two known approaches of generative Quantum Machine Learning algorithms are revised to demonstrate the encoding capability and sampling benets of qubits. As one possible extension of those generative models, the Quantum-assisted Generator (QaG) is presented which implements a qubit layer coupled to subsequent classical layers. The QaG provides promising indications that quantum phenomena may enhance performance of a generative neural network. This work also considers Hamiltonian-based and gate-based implementations of quantum-assisted Autoencoders. Using an eective hybrid training approach consisting of simultaneous application of conventional backpropagation and blackbox optimization, we show that the Hamiltonian-based Autoencoder is able to learn the MNIST data set with good generalization properties. Several approaches to extend the presented quantum-assisted algorithms are proposed to further investigate potential benets of utilizing quantum systems in combination with Articial Neural Networks.