Spiking neuromorphic chip learns entangled quantum states
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2020
- DOI:
- 10.48550/arXiv.2008.01039
- arXiv:
- arXiv:2008.01039
- Bibcode:
- 2020arXiv200801039C
- Keywords:
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- Computer Science - Emerging Technologies;
- Condensed Matter - Disordered Systems and Neural Networks;
- Computer Science - Neural and Evolutionary Computing;
- Quantum Physics
- E-Print:
- 9+13 pages, 4+2 figures