Characterizing physical and logical errors in a transversal CNOT via cycle error reconstruction

• URL: http://arxiv.org/abs/2504.11980v1
• Date: Wed, 16 Apr 2025 11:22:32 GMT
• Title: Characterizing physical and logical errors in a transversal CNOT via cycle error reconstruction
• Authors: Nicholas Fazio, Robert Freund, Debankan Sannamoth, Alex Steiner, Christian D. Marciniak, Manuel Rispler, Robin Harper, Thomas Monz, Joseph Emerson, Stephen D. Bartlett,
• Abstract summary: advancing towards fault-tolerant quantum computing will require an understanding of the underlying error mechanisms in logical primitives.<n>We demonstrate the novel capability to characterize the physical error properties relevant to fault-tolerant operations via cycle error reconstruction.
• Score: 0.24075310817964646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of prototype quantum information processors has progressed to a stage where small instances of logical qubit systems perform better than the best of their physical constituents. Advancing towards fault-tolerant quantum computing will require an understanding of the underlying error mechanisms in logical primitives as they relate to the performance of quantum error correction. In this work we demonstrate the novel capability to characterize the physical error properties relevant to fault-tolerant operations via cycle error reconstruction. We illustrate this diagnostic capability for a transversal CNOT, a prototypical component of quantum logical operations, in a 16-qubit register of a trapped-ion quantum computer. Our error characterization technique offers three key capabilities: (i) identifying context-dependent physical layer errors, enabling their mitigation; (ii) contextualizing component gates in the environment of logical operators, validating the performance differences in terms of characterized component-level physics, and (iii) providing a scalable method for predicting quantum error correction performance using pertinent error terms, differentiating correctable versus uncorrectable physical layer errors. The methods with which our results are obtained have scalable resource requirements that can be extended with moderate overhead to capture overall logical performance in increasingly large and complex systems.

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