A hybrid method uses fixed quantum annealing states as boundary resources for classical MERA tensor networks to improve ground-state approximations without deeper quantum circuits.
Matrix product channel: Variationally optimized quantum tensor network to mitigate noise and reduce errors for the variational quantum eigensolver
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A differentiable tensor-network framework learns CPTP noise channels from single-circuit measurement data on IBM hardware and generalizes the model to unrelated circuits.
citing papers explorer
-
Combining non-parametric quantum states and MERA tensor networks for ground-state optimization
A hybrid method uses fixed quantum annealing states as boundary resources for classical MERA tensor networks to improve ground-state approximations without deeper quantum circuits.
-
Quantum hardware noise learning via differentiable Kraus representation on tensor networks
A differentiable tensor-network framework learns CPTP noise channels from single-circuit measurement data on IBM hardware and generalizes the model to unrelated circuits.