Iterative-QAOA solves pangenome assembly instances on current quantum hardware by using a fixed-ramp QAOA schedule with warm-start updates and a new HUBO encoding that cuts variables from O(N^{2}) to O(N log N).
Iterative quantum optimisation with a warm-started quantum state
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
use method 1representative citing papers
An iterative nonvariational quantum algorithm using warm-start states and classically computed imaginary time evolution circuits achieves median solutions within 95% of optimal for MaxCut on small 3-regular graphs using only 100 shots, outperforming random and basic classical searches.
citing papers explorer
-
Nonvariational quantum optimisation approaches to pangenome-guided sequence assembly
Iterative-QAOA solves pangenome assembly instances on current quantum hardware by using a fixed-ramp QAOA schedule with warm-start updates and a new HUBO encoding that cuts variables from O(N^{2}) to O(N log N).
-
Iterative warm-start optimization with quantum imaginary time evolution
An iterative nonvariational quantum algorithm using warm-start states and classically computed imaginary time evolution circuits achieves median solutions within 95% of optimal for MaxCut on small 3-regular graphs using only 100 shots, outperforming random and basic classical searches.