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SUMMARY:Key Ideas in Quantum Machine Learning - Rakesh Achutha\, Universit
 y of Cambridge
DTSTART:20260225T110000Z
DTEND:20260225T120000Z
UID:TALK245107@talks.cam.ac.uk
CONTACT:Rachel Zhang
DESCRIPTION:This talk introduces key ideas in quantum machine learning\, w
 ith a focus on hybrid quantum–classical generative models. I will presen
 t our recent work on QTabGAN\, a hybrid quantum–classical GAN designed f
 or realistic tabular data synthesis. The model uses variational quantum ci
 rcuits to learn expressive probability distributions\, combined with class
 ical neural networks to generate structured tabular data.\nAlongside the t
 echnical details of the model and experimental results\, the talk will als
 o discuss the broader role of quantum computing in generative modelling\, 
 its potential advantages for complex data distributions\, and practical ch
 allenges for near-term quantum hardware.\nhttps://arxiv.org/pdf/2602.12704
LOCATION:MR10\, Centre for Mathematical Sciences
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