Error Mitigation with Artificial Symmetries
ORAL
Abstract
Incoherent noise arising from imperfect control and measurement presents a serious obstacle to efforts to apply noisy intermediate-scale quantum (NISQ) computation to meaningful problems. We present an error mitigation technique that reduces the error in the estimation of expectation values by introducing artificial symmetries more amenable to NISQ devices than traditional quantum error correcting codes. As an example, we present some numerical data showing the effectivenes of our technique applied to the time evolution of a one-dimensional Heisenberg chain. We show that our technique can provide more than an order of magnitude reduction in error over a wide range of noise strengths and system sizes and analytically characterize its expected performance in a few simple limits.
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Presenters
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William Huggins
University of California, Berkeley, Google LLC
Authors
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William Huggins
University of California, Berkeley, Google LLC
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Sam McArdle
Google LLC
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Thomas O'Brien
Google LLC
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Joonho Lee
Chemistry, Columbia University
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Nicholas Rubin
Google Quantum AI, Google Inc., Google LLC, Google
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Birgitta K Whaley
University of California, Berkeley, Chemistry, University of California, Berkeley
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Ryan Babbush
Google Quantum AI, Google LLC
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Jarrod McClean
Google, Google LLC