Gerasimos Angelatos on Muck Rack

Gerasimos Angelatos

As seen in: Nature, Physical Review B
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Articles

Predicting chaotic dynamics on NISQ hardware with quantum reservoir networks

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By Ethan Evans , Gerasimos Angelatos, Vignesh Narayanan, Erik L. Connerty
| Nature
Abstract Recent advances in artificial intelligence have highlighted the remarkable capabilities of neural network (NN)-powered systems on classical computers. However, these systems face significant computational challenges that limit scalability and efficiency. Here, we propose a quantum reservoir network (QRN) algorithm for prediction and reconstruction of dynamical systems with current quantum hardware.
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Publisher Correction: Overcoming the coherence time barrier in quantum machine learning on temporal data - Nature Communications

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By Fangjun Hu, Saeed Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham Rowlands
| Nature
Correction to: Nature Communications https://doi.org/10.1038/s41467-024-51162-7, published online 30 August 2024 In the original version of this Article, the following errors were present: The bibliography was missing a reference, “Martínez-Peña, R. & Ortega, J.-P. Quantum reservoir computing in finite dimensions. Phys. Rev. E 107, 035306 (2023)” which should have appeared at number [53]. Because of the above, two mentions of ref.
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Overcoming the coherence time barrier in quantum machine learning on temporal data - Nature Communications

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By Fangjun Hu, Saeed Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham Rowlands
| Nature
Abstract The practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence.
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