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Trapped Ion Quantum Computing

Quantum reservoir processing

arXiv
Authors: Sanjib Ghosh, Andrzej Opala, Michał Matuszewski, Tomasz Paterek, Timothy C. H. Liew

Year

2018

Paper ID

23120

Status

Preprint

Abstract Read

~2 min

Abstract Words

108

Citations

N/A

Abstract

The concurrent rise of artificial intelligence and quantum information poses opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a non-linear function of an input quantum state (e.g. entropy, purity or logarithmic negativity). In this way experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.

Why This Paper Matters

  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
  • It adds a 2018 reference point for readers tracking recent quantum research.
  • The concurrent rise of artificial intelligence and quantum information poses opportunity for creating interdisciplinary technologies like quantum neural networks.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1811.10335 [2] arXiv https://arxiv.org/abs/1811.10335 [3] Publisher https://arxiv.org/abs/1811.10335

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