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Efficient learning of Sparse Pauli Lindblad models for fully connected qubit topology
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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).
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Cited by 2 Pith papers
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A hybrid Lindblad-master-equation noise model with 10 parameters per qubit and 3 per pair predicts RB, dynamical-decoupling, and H2 VQE dynamics on IBM transmon hardware, reaching 0.5% relative energy error at the opt...
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