Computational phase transitions in decision problems exhibit a detectable signature in Gibbs distributions that can be observed in physical annealing processors.
Ising formulations of many np problems.Frontiers in Physics, 2:5
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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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Computational Phase Transition Signature in Gibbs Sampling
Computational phase transitions in decision problems exhibit a detectable signature in Gibbs distributions that can be observed in physical annealing processors.
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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.