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Quantum machine learning

Quantum Machine Learning (QML) studies how quantum compu­ters can enhance data-driven models. Classi­cal machine learning repres­ents data as bit strings and manipu­la­tes vectors and matri­ces with linear algebra. QML replaces or augments these steps with quantum states and quantum circuits, hoping for faster training, impro­ved sampling, or funda­men­tally new model classes. Key approa­ches include varia­tio­nal quantum circuits, optimi­zed like neural networks, and quantum kernel methods that embed data in high-dimen­sio­nal Hilbert spaces. On today’s Noisy Inter­me­diate-Scale Quantum (NISQ) devices, QML serves as a testbed for near-term quantum advan­tage in chemis­try, finance, and optimiza­tion, and drives co-design of algorithms, hardware, and error mitigation.

Topics in the Subfield Quantum machine learning

Select a topic within the subfield Quantum machine learning

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…

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…

More selected Quantum Compu­ting Software:

Select a subfield within Quantum Compu­ting Software

Software for quantum compu­ting hardware

Software for quantum compu­ting hardware forms the layer between high level algorithms and physi­cal qubits. At the top of the stack…

Quantum Simula­tion of Quantum Systems

The quantum simula­tion of quantum systems focuses on the modeling of inter­ac­ting quantum mecha­ni­cal partic­les, where classi­cal methods, due to…

Quantum Optimiza­tion

Quantum optimiza­tion explo­res how quantum compu­ters can be used to solve complex optimiza­tion problems more effici­ently. Classi­cal optimiza­tion often formu­la­tes such problems as…

Quantum error correction

Quantum Error Correc­tion (QEC) provi­des the archi­tec­tu­ral founda­tion for relia­ble quantum compu­ting, as it addres­ses the…

Crypto­gra­phy

Today’s widely used encryp­tion methods, which secure the vast majority of inter­net traffic, are based on the…

Quantum software engineering

Quantum Service Enginee­ring refers to the appli­ca­tion of service orien­ted enginee­ring princi­ples, methods, and tools to the design, imple­men­ta­tion, and delivery of quantum compu­ting capabi­li­ties as…

Founda­ti­ons

Quantum theory raises concep­tual and mathe­ma­ti­cal questi­ons, including…

Uncon­ven­tio­nal quantum compu­ting paradigms

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