QAOA achieves approximation ratios of 0.90-0.95 on N=5-20 Euclidean graphs, outperforming classical baselines by 2.7-4.4% with 2-3x faster runtimes and picojoule-scale energy use, projecting 8.2% real-world routing efficiency gains and 2.62 EJ annual US fuel savings.
Quantum Boltzmann Machine
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Quantum annealing is described as a heuristic for discrete optimization and sampling that also serves as a platform for studying non-equilibrium many-body quantum dynamics with programmable spin systems.
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Potential Energy Savings from Quantum Computing-Based Route Optimization
QAOA achieves approximation ratios of 0.90-0.95 on N=5-20 Euclidean graphs, outperforming classical baselines by 2.7-4.4% with 2-3x faster runtimes and picojoule-scale energy use, projecting 8.2% real-world routing efficiency gains and 2.62 EJ annual US fuel savings.
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Quantum Annealing: Optimisation, Sampling, and Many-Body Dynamics
Quantum annealing is described as a heuristic for discrete optimization and sampling that also serves as a platform for studying non-equilibrium many-body quantum dynamics with programmable spin systems.