AI RESEARCH
Scalable Quantum Error Mitigation with Physically Informed Graph Neural Networks
arXiv CS.LG
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ArXi:2604.16815v1 Announce Type: cross Quantum error mitigation (QEM) provides a practical route for estimating reliable observables on noisy intermediate-scale quantum (NISQ) devices. Traditional QEM strategies, including zero-noise extrapolation (ZNE) and Clifford data regression (CDR), rely on noise scaling or global regression, and their performance is constrained by the exponential growth of the system degrees of freedom. We construct a graph-enhanced mitigation (GEM) framework, which incorporates physical information into the model representation.