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).
Quantum alternating operator ansatz (QAOA) beyond low depth with gradually changing unitaries
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Iterative orthogonal-basis interpolation constructs high-quality QAOA parameter schedules for depths exceeding 1000 layers, outperforming prior methods on SK, portfolio, and LABS benchmarks.
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
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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).
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Iterative Interpolation Schedules for Quantum Approximate Optimization Algorithm
Iterative orthogonal-basis interpolation constructs high-quality QAOA parameter schedules for depths exceeding 1000 layers, outperforming prior methods on SK, portfolio, and LABS benchmarks.
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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.
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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.