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Efficient learning of quantum noise

Cite this dataset

Harper, Robin; Flammia, Steven; Wallman, Joel (2020). Efficient learning of quantum noise [Dataset]. Dryad.


Noise is the central obstacle to building large-scale quantum computers. Quantum systems with sufficiently  uncorrelated and weak noise could be used to solve computational problems that are intractable with current digital computers. There has been substantial progress towards engineering such systems. However, continued progress depends on the ability to characterize quantum noise reliably and efficiently with high precision. Here we introduce a protocol that comprehensively and efficiently characterizes the error rates of quantum noise and we experimentally implement it on a 14-qubit superconducting quantum architecture. The method returns an estimate of the effective noise with relative precision and can detect arbitrary correlated errors. We show how to construct a quantum noise correlation matrix allowing the easy visualization of all pairwise correlated errors, enabling the discovery of long-range two-qubit correlations in the 14 qubit device that had not previously been detected. These properties of the protocol make it exceptionally well suited for high-precision noise metrology in quantum information processors. Our results are the first implementation of a provably rigorous, full diagnostic protocol capable of being run on state of the art devices and beyond. These results pave the way for noise metrology in next-generation quantum devices, calibration in the presence of crosstalk, bespoke quantum error-correcting codes, and customized fault-tolerance protocols that can greatly reduce the overhead in a quantum computation.


Data was collected as a result of running a protocol on the IBM Quantum Experience (Melbourne). The results of runs are stored as python pickles. And is provided here, along with instructions as to how to read the pickles.

Julia code showing how the data was analysed and plots in the paper were created is available from the authors on request.

Also provided here is the Mathematica code used to generate the data for and create the plots in figure 4 of the paper.


United States Army Research Office, Award: W911NF-14-1-0098

United States Army Research Office, Award: W911NF-14-1-0103

Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS), Award: CE170100009

Government of Ontario through Canada First Research Excellence Fund, Transformative Quantum Technologies and Natural Sciences and Engineering Research Council

Innovation, Science and Economic Development Canada