A physics-inspired recurrent network, NNAS, estimates layer-wise noise impact and mitigates errors in simulated QAOA and GHZ circuits, using an order of magnitude less training data and less sampling overhead than standard QEM baselines.
Efficient learning of Sparse Pauli Lindblad models for fully connected qubit topology
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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).
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Physics-inspired Machine Learning for Quantum Error Mitigation
A physics-inspired recurrent network, NNAS, estimates layer-wise noise impact and mitigates errors in simulated QAOA and GHZ circuits, using an order of magnitude less training data and less sampling overhead than standard QEM baselines.