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Quantum Information Processing
Article . 2003 . Peer-reviewed
License: Springer Nature TDM
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https://dx.doi.org/10.48550/ar...
Article . 2003
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Experimental Demonstration of Quantum Lattice Gas Computation

Experimental demonstration of quantum lattice gas computation
Authors: Marco A. Pravia; Zhiying Chen; Jeffrey Yepez; David G. Cory;

Experimental Demonstration of Quantum Lattice Gas Computation

Abstract

We report an ensemble nuclear magnetic resonance (NMR) implementation of a quantum lattice gas algorithm for the diffusion equation. The algorithm employs an array of quantum information processors sharing classical information, a novel architecture referred to as a type-II quantum computer. This concrete implementation provides a test example from which to probe the strengths and limitations of this new computation paradigm. The NMR experiment consists of encoding a mass density onto an array of 16 two-qubit quantum information processors and then following the computation through 7 time steps of the algorithm. The results show good agreement with the analytic solution for diffusive dynamics. We also describe numerical simulations of the NMR implementation. The simulations aid in determining sources of experimental errors, and they help define the limits of the implementation.

20 pages, 3 figures

Keywords

Quantum Physics, Quantum cryptography, quantum key distribution, Quantum computation, FOS: Physical sciences, quantum communication, Experimental work for problems pertaining to quantum theory, Quantum Physics (quant-ph), quantum information processing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Average
Top 10%
Average
Green