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QNNs

Quantum Neural Networks (QNNs) are machine learning models which use trainable parame­ter­i­zed quantum circuits. Like classi­cal neural networks, they map inputs to outputs by adjus­ting parame­ters to minimize a loss function. In practice, QNNs are often hybrid: a classi­cal compu­ter propo­ses parame­ter updates, while a quantum proces­sor evalua­tes circuit outputs (usually expec­ta­tion values). QNNs can be used for classi­fi­ca­tion and regres­sion, feature learning, and physics-inspi­red modeling. Key conside­ra­ti­ons include how data are encoded into quantum states, circuit design for expres­si­vity and traina­bi­lity, and how hardware noise and finite sampling affect training and generalization.

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…

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…

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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