Quantum machine learning (QML)---machine learning on quantum computers---is a rapidly developing, promising application of near-term quantum devices. Most present hardware implements the unitary circuit model of quantum computation. However, recently also measurement-based quantum computation (MBQC) has become technologically relevant, and progress is made towards MBQC with atomic systems. So far, measurement-based QML (MB-QML) has almost not been considered. We introduce a general framework for MB-QML on classical and quantum data. Specifically, we propose a universal building block to construct ansätze for unitaries, measurements, and channels, and discuss how to constrain these ansätze. Our algorithm successfully learns unitaries and classifies quantum states for small systems, proving robust to noise. This work lays the foundation for QML on MBQC devices.
|Presenter name||Polina Feldmann|
|How will you attend ICAP-27?||I am planning on in-person attendance|