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

TL;DR

This paper extends the theoretical framework for learning Sparse Pauli-Lindblad noise models from linearly connected qubits to fully connected qubit systems, aiding in error mitigation for diverse quantum hardware.

Contribution

It generalizes the learning method for Sparse Pauli-Lindblad models to fully connected qubit topologies, broadening applicability to ion trap quantum devices.

Findings

Theoretical extension to fully connected qubit systems.

Enhanced noise modeling for ion trap hardware.

Potential improvements in error mitigation techniques.

Abstract

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).