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Lossy compression of statistical data using quantum annealer.

Boram Yoon1, Nga T T Nguyen2, Chia Cheng Chang3,4,5,6

  • 1CCS-7, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. boram@lanl.gov.

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This summary is machine-generated.

This study introduces a novel lossy compression algorithm for statistical data using binary variables. Simulated annealing achieved superior compression, outperforming neural networks, while quantum annealing showed potential despite hardware limitations.

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Area of Science:

  • Computational Physics
  • Data Science
  • Quantum Computing

Background:

  • Lossy compression is crucial for managing large datasets in scientific simulations.
  • Existing methods may not optimally balance compression ratio and data fidelity for statistical floating-point data.
  • Representation learning offers a promising avenue for developing advanced compression techniques.

Purpose of the Study:

  • To develop and evaluate a novel lossy compression algorithm for statistical floating-point data.
  • To compare the efficacy of simulated annealing and quantum annealing for coefficient retrieval in this compression scheme.
  • To introduce a bias correction method for improving data analysis post-compression.

Main Methods:

  • A representation learning approach using binary variables to reconstruct statistical data.
  • Classical optimization for basis vectors and comparative analysis of simulated and quantum annealing for binary coefficients.
  • Implementation of a bias correction procedure to mitigate reconstruction errors.
  • Demonstration on lattice quantum chromodynamics (LQC) simulation datasets.

Main Results:

  • The simulated annealing approach yielded 3-3.5 times better compression performance compared to a neural network autoencoder.
  • Quantum annealing demonstrated potential but was constrained by quantum processing unit (QPU) control errors, leading to uncertainties.
  • The D-Wave Advantage system showed a higher likelihood of finding low-energy solutions compared to the D-Wave 2000Q.

Conclusions:

  • The proposed lossy compression algorithm, particularly with simulated annealing, offers significant improvements for statistical data.
  • Quantum annealing presents a viable, albeit currently limited, alternative for data compression tasks.
  • Hardware advancements in QPUs are critical for unlocking the full potential of quantum annealing in scientific data compression.