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Our simulations show promising results, and may break new ground towards the use of quantum memristors in quantum neuromorphic architectures.doi:10.1038/s41566-022-00973-5Nature Publishing Group UKNature Photonics
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Memory capacity remains one of the enduring challenges to quantum computing. Now, Vienna researchers say they have found a way to scale quantum memristors. Researchers at the University of Vienna in Austria believe they have discovered a new way to develop a quantum memristor that can be replicate...
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Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved energy efficiency compared to digital electronic neural networks. However,
With the ever-growing demand for a greater number of pixels, next-generation displays have challenging requirements for resolution as well as colour gamut. Here, to meet this need, quantum-dot light-emitting diodes (QLEDs) with an ultrahigh pixel resolut
Ohannessian et al., Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing. Nat. Commun. 13, 2074 (2022). https://doi.org/10.1038/ s41467-022-29727-1 143. S. Zhou, G. Zhou, Y. Li, X. Xu, Y.J. Hsu et al., Understanding ...
Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being ...