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Excited to present my research on Investigating Quantum Boltzmann Machines for classification at the European quantum artificial intelligence summer school 2023! 🧪📊 We explore different strategies to build Hamiltonians for QBMs. 🌌🚀 #QuantumComputing #QML

Fascinating insights into Quantum Boltzmann Machine and Symmetries in the Data by Maria Demidik from DESY (Deutsch). Don't miss out the next session!

Are you trying to prepare a Gibbs state on a near-term device using VarQITE, but don't know how to design your circuit? 😱 Then, check this out 👇 📢We combine internal symmetries 👉👈 with time reversal symmetry ⏳ to supercharge ansatz design ⚡️ scirate.com/arxiv/2307.135…

It was a pleasure to share insights and connect with an amazing audience. Thanks Helmholtz AI for creating such a valuable seminar series.

Check our work on GQML 🚀

The impact of hardware noise on the capabilities of equivariant quantum neural networks is explored, and strategies for mitigation are addressed. Cenk Tüysüz Su Yeon Chang Maria Demidik Michele Grossi DESY CQTA CERN Quantum Technology Initiative (CERN QTI) go.aps.org/3y0gTou

📢 Low-dimensional quantum Boltzmann machines (QBMs) can learn high-dimensional distributions and they are trainable 💅 We all read about many recent results advocating against many types of QNNs. I invite you to consider a long-forgotten model: fully-visible QBM. a thread 🧵