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Unsuper­vi­sed QML

Unsuper­vi­sed Quantum Machine Learning (UQML) is a field that explo­res how quantum compu­ters can find hidden patterns in data without being told what to look for. In “super­vi­sed” learning, a compu­ter is trained with labeled examp­les (like “this is a cat”). In “unsuper­vi­sed” learning, the machine looks at raw data and finds groups or anoma­lies on its own. By using quantum proper­ties like super­po­si­tion (being in multi­ple states at once), unsuper­vi­sed QML algorithms can theore­ti­cally sort through massive amounts of complex data more effec­tively than today’s fastest super­com­pu­ters. This makes it a promi­sing tool for disco­ve­ring new chemi­cals, detec­ting credit card fraud, or organi­zing large finan­cial 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…

Super­vi­sed QML

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

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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