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Super­vi­sed QML

Super­vi­sed Machine Learning (ML) is a founda­tio­nal algorith­mic techni­que develo­ped to learn patterns from label­led training data—pairs of inputs and known outputs—to predict outco­mes for new, unseen data. Over the past decades, scien­tists have been trying to harvest quantum pheno­mena like super­po­si­tion and entan­gle­ment to process infor­ma­tion beyond classi­cal capabi­li­ties. The merge of these two ideas created the field of Super­vi­sed Quantum Machine Learning (QML), where a quantum compu­ter is used to perform the super­vi­sed training and predic­tion tasks to gain advan­ta­ges over classi­cal ML. There are already two well stablished sources of advan­ta­ges; speedups for inference and/or enhan­ced expres­si­vity, making QML the ideal method to process and/or model complex, large-scale datasets.

Topics in the Subfield Quantum machine learning:

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Genera­tive QML

In genera­tive quantum machine learning (GQML), the objec­tive is to learn a model from a given set of data points that can…

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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