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quant-ph2025
Learning Minimal Representations of Fermionic Ground States
Felix Frohnert, Emiel Koridon, Stefano Polla
We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network…
quant-ph2025
Ab Initio Polaritonic Chemistry on Diverse Quantum Computing Platforms: Qubit, Qudit, and Hybrid Qubit-Qumode Architectures
Even Chiari, Wafa Makhlouf, Lucie Pepe +5
Trying to export ab initio polaritonic chemistry onto emerging quantum computers raises fundamental questions. A central one is how to efficiently represent both fermionic and boso…
quant-ph2024
Learning Density Functionals from Noisy Quantum Data
Emiel Koridon, Felix Frohnert, Eric Prehn +3
The search for useful applications of noisy intermediate-scale quantum (NISQ) devices in quantum simulation has been hindered by their intrinsic noise and the high costs associated…