A resource-estimation framework places CVRP instances on a go/no-go decision diagram and shows that a higher-order encoding needs far fewer qubits than standard QUBO for early-advantage benchmarks such as Golden-5.
Benchmarking the quantum approximate optimization algorithm
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QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
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.
QAOA with default parameters is compared per-shot to Goemans-Williamson on realistic Max-Cut instances, highlighting practical limitations under black-box use.
Absence of quantum advantage for log-depth QAOA on the binary paint shop problem implies a classical mean-field algorithm achieving a paint-swap ratio of approximately 0.2799, outperforming known heuristics and quantum methods.
citing papers explorer
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Requirements for Early Quantum Utility and Quantum Utility in the Capacitated Vehicle Routing Problem
A resource-estimation framework places CVRP instances on a go/no-go decision diagram and shows that a higher-order encoding needs far fewer qubits than standard QUBO for early-advantage benchmarks such as Golden-5.
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Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
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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.
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Per-Shot Evaluation of QAOA on Max-Cut: A Black-Box Implementation Comparison with Goemans-Williamson
QAOA with default parameters is compared per-shot to Goemans-Williamson on realistic Max-Cut instances, highlighting practical limitations under black-box use.
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No quantum advantage implies improved bounds and classical algorithms for the binary paint shop problem
Absence of quantum advantage for log-depth QAOA on the binary paint shop problem implies a classical mean-field algorithm achieving a paint-swap ratio of approximately 0.2799, outperforming known heuristics and quantum methods.