A deep-learned error mitigation method using partially knitted circuits reduces VQE energy error to below 1% under realistic noise, outperforming ZNE and damping-factor methods.
Variational quantum eigensolver for the 1D quantum Ising model Our testbed for the VQE algorithm is the 1D quantum Ising model with nearest-neighbor random couplings
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Deep-learned error mitigation via partially knitted circuits for the variational quantum eigensolver
A deep-learned error mitigation method using partially knitted circuits reduces VQE energy error to below 1% under realistic noise, outperforming ZNE and damping-factor methods.