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Quantum machine learning on near-term devices

Paper Authors:

Yaswitha Gujju,

Atsushi Matsuo,

Rudy Raymond

Bullets

Key Details

Reviews quantum machine learning approaches on real hardware

Focuses on supervised and unsupervised techniques

Examines variational circuits and kernel methods

Discusses techniques to address hardware limitations

Compares performance to classical methods

AI generated summary

Quantum machine learning on near-term devices

This paper reviews the current state of quantum machine learning techniques implemented on real quantum hardware. It focuses on key supervised and unsupervised learning applications, examining approaches like variational quantum circuits and quantum kernel methods. The review highlights techniques used to address hardware limitations, and compares performance to classical methods.

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