Efficient learning of Sparse Pauli Lindblad models for fully connected qubit topology

The challenge to achieve practical quantum computing considering current hardware size and gate fidelity is the sensitivity to errors and noise. Recent work has shown that by learning the underlying noise model capturing qubit cross-talk, error mitigation can push the boundary of practical quantum c...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Jose Este Jaloveckas, Minh Tham Pham Nguyen, Lilly Palackal, Lorenz, Jeanette Miriam, Ehm, Hans
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.11.2023
Subjects
Crosstalk
Hardware
Learning
Noise sensitivity
Quantum computing
Qubits (quantum computing)
Topology
Online AccessGet full text

Cover

More Information
Summary:The challenge to achieve practical quantum computing considering current hardware size and gate fidelity is the sensitivity to errors and noise. Recent work has shown that by learning the underlying noise model capturing qubit cross-talk, error mitigation can push the boundary of practical quantum computing. This has been accomplished using Sparse Pauli-Lindblad models only on devices with a linear topology connectivity (i.e. superconducting qubit devices). In this work we extend the theoretical requirement for learning such noise models on hardware with full connectivity (i.e. ion trap devices).
ISSN:2331-8422