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Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

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Abstract

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. We present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

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Acknowledgements

The authors are indebted to Dr. Richard Archibald of the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) for reviewing the manuscript. This work was in part supported by the United States Department of Defense (DoD) and used resources of the Computational Research and Development Programs at ORNL. ORNL is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. S.C. acknowledges DOE ASCR funding under the Quantum Computing Application Teams program, FWP No. ERKJ347.

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  1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109-2218, USA

    Maryam Bagherian

  2. The Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109-2218, USA

    Maryam Bagherian

  3. Quantum Information Science, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6211, USA

    Sarah Chehade & Ali Passian

  4. Mathematics in Computations, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6211, USA

    Ben Whitney

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Bagherian, M., Chehade, S., Whitney, B. et al. Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction. Computing 105, 1419–1465 (2023). https://doi.org/10.1007/s00607-023-01154-0

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