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- Title
Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics.
- Authors
Zhukov, Andrey; Pogosov, Walter
- Abstract
We address a learning-based quantum error mitigation method, which utilizes deep neural network applied at the postprocessing stage, and study its performance in the presence of different types of quantum noises. We concentrate on the simulation of Trotterized dynamics of 2D spin lattice in the regime of high noise, when expectation values of bounded traceless observables are strongly suppressed. By using numerical simulations, we demonstrate a dramatic improvement of data quality for both local weight-1 and weight-2 observables for the depolarizing and inhomogeneous Pauli channels. At the same time, the effect of coherent ZZ crosstalks is not mitigated, so that in practice crosstalks should be at first converted into incoherent errors by randomized compiling.
- Subjects
ARTIFICIAL neural networks; QUANTUM noise; DEEP learning; DATA quality
- Publication
Quantum Information Processing, 2024, Vol 23, Issue 3, p1
- ISSN
1570-0755
- Publication type
Article
- DOI
10.1007/s11128-024-04296-y