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Genera­tive QML

In genera­tive quantum machine learning (GQML), a model is trained on given data points to appro­xi­mate an unknown proba­bi­lity distri­bu­tion from which reali­stic new samples can be genera­ted. The model consists of a parame­ter­i­zed quantum circuit. Recent work demons­tra­tes that certain GQML models could achieve a quantum advan­tage: sampling requi­res a quantum compu­ter, while training can be perfor­med entirely on classi­cal compu­ters using specia­li­zed simula­tion techni­ques. The under­ly­ing asymme­try arises because training only requi­res local expec­ta­tion values of the quantum circuits, whereas sampling demands the complete inter­fe­rence struc­ture of the output distribution.

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Super­vi­sed QML

Super­vi­sed machine learning (ML) is a funda­men­tal algorith­mic approach that enables…

Unsuper­vi­sed QML

Unsuper­vi­sed quantum machine learning explo­res how quantum compu­ters can identify hidden patterns in data without…

Kernel

Kernel methods map data with complex, nonlinear relati­onships into a high dimen­sio­nal feature space in order to…

QNNs

Quantum neural networks (QNNs) are machine learning models that…

QELM

Tradi­tio­nal approa­ches to super­vi­sed learning based on neural network archi­tec­tures (NNs) require, during the…

Traina­bi­lity

Traina­bi­lity in quantum machine learning descri­bes how…

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