The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structural and dynamical properties of the biological brain, show a high energy efficiency in running artificial neural-network architectures for the profit of generative applications. This encourages employing such hardware systems as platforms for simulations of quantum systems. Here we report on the realization of a prototype using the latest spike-based BrainScaleS hardware allowing us to represent few-qubit maximally entangled quantum states with high fidelities. Bell correlations of pure and mixed two-qubit states are well captured by the analog hardware, demonstrating an important building block for simulating quantum systems with spiking neuromorphic chips.
@article{arxiv.2008.01039,
title = {Spiking neuromorphic chip learns entangled quantum states},
author = {Stefanie Czischek and Andreas Baumbach and Sebastian Billaudelle and Benjamin Cramer and Lukas Kades and Jan M. Pawlowski and Markus K. Oberthaler and Johannes Schemmel and Mihai A. Petrovici and Thomas Gasenzer and Martin Gärttner},
journal= {arXiv preprint arXiv:2008.01039},
year = {2022}
}