Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
Accelerating feedback-based quantum algorithms through time rescaling
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
extension 1
citation-polarity summary
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2roles
extension 1polarities
extend 1representative citing papers
A PUBO formulation of MST combined with the FALQON algorithm selects prototypes for OPF classifiers and achieves accuracies comparable to classical Prim's algorithm while reducing qubit requirements.
citing papers explorer
-
Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
-
PUBO Formulation for MST and Application to Optimum-Path Forest
A PUBO formulation of MST combined with the FALQON algorithm selects prototypes for OPF classifiers and achieves accuracies comparable to classical Prim's algorithm while reducing qubit requirements.