Capturing exponential variance using polynomial resources: applying tensor networks to nonequilibrium stochastic processes.

@article{Johnson2015CapturingEV,
  title={Capturing exponential variance using polynomial resources: applying tensor networks to nonequilibrium stochastic processes.},
  author={T. Johnson and T. J. Elliott and S. R. Clark and D. Jaksch},
  journal={Physical review letters},
  year={2015},
  volume={114 9},
  pages={
          090602
        }
}
  • T. Johnson, T. J. Elliott, +1 author D. Jaksch
  • Published 2015
  • Physics, Medicine
  • Physical review letters
  • Estimating the expected value of an observable appearing in a nonequilibrium stochastic process usually involves sampling. If the observable's variance is high, many samples are required. In contrast, we show that performing the same task without sampling, using tensor network compression, efficiently captures high variances in systems of various geometries and dimensions. We provide examples for which matching the accuracy of our efficient method would require a sample size scaling… CONTINUE READING

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