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).
Toward a linear- ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
fields
quant-ph 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
Exascale classical simulation validates noise-tolerant performance of a 98-qubit QPU up to 48 qubits for LR-QAOA, with statistical analysis showing coherent regime up to 93 qubits before outputs become indistinguishable from random.
Hybrid Iterative-QAOA warm starts improve shipment delivery by up to 12% and cut drive distance by 6% on real logistics data when fed to a classical solver.
Hybrid quantum-classical graph partitioning inside LS-DYNA reduces amortized wall-clock time for large FEA simulations by 5.9-14.6 percent on meshes up to 35 million elements.
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).
-
Large-Scale Quantum Circuit Simulation on an Exascale System for QPU Benchmarking
Exascale classical simulation validates noise-tolerant performance of a 98-qubit QPU up to 48 qubits for LR-QAOA, with statistical analysis showing coherent regime up to 93 qubits before outputs become indistinguishable from random.
-
Hybrid Quantum-Classical Optimization Workflows for the Shipment Selection Problem
Hybrid Iterative-QAOA warm starts improve shipment delivery by up to 12% and cut drive distance by 6% on real logistics data when fed to a classical solver.
-
End-to-end performance of quantum-accelerated large-scale linear algebra workflows
Hybrid quantum-classical graph partitioning inside LS-DYNA reduces amortized wall-clock time for large FEA simulations by 5.9-14.6 percent on meshes up to 35 million elements.