125 citations · 462 across the 16 of their papers we have counts for
16 papers
A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
Owen Lockwood, Jérémy Béjanin, Joost Bus +4
To help address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack tha…
The effect of the processing and measurement operators on the expressive power of quantum models
Aikaterini, Gratsea, Patrick Huembeli
There is an increasing interest in Quantum Machine Learning (QML) models, how they work and for which applications they could be useful. There have been many different proposals on…
Towards a scalable discrete quantum generative adversarial neural network
Smit Chaudhary, Patrick Huembeli, Ian MacCormack +3
We introduce a fully quantum generative adversarial network intended for use with binary data. The architecture incorporates several features found in other classical and quantum m…
Entanglement Forging with generative neural network models
Patrick Huembeli, Giuseppe Carleo, Antonio Mezzacapo
The optimal use of quantum and classical computational techniques together is important to address problems that cannot be easily solved by quantum computations alone. This is the…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Hessian-based toolbox for reliable and interpretable machine learning in physics
Anna Dawid, Patrick Huembeli, Michał Tomza +2
Machine learning (ML) techniques applied to quantum many-body physics have emerged as a new research field. While the numerical power of this approach is undeniable, the most expre…