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Spiking neuromorphic chip learns entangled quantum states
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years.
Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning
Machine learning has the potential to aid our understanding of phase structures in lattice quantum field theories through the statistical analysis of Monte Carlo samples.
Spectral Reconstruction with Deep Neural Networks
We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem.
The Discrete Langevin Machine: Bridging the Gap Between Thermodynamic and Neuromorphic Systems
A formulation of Langevin dynamics for discrete systems is derived as a class of generic stochastic processes.
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