Adaptive Differential Evolution and Multistart Search for Noisy QAOA Optimization

arXiv:2609.23180 · quant-ph, cs.NE · Submitted 2026-09-19 · Read on arXiv

quant-ph, cs.NE

Submitted: 2026-09-19

Updated: 2026-09-19

Terminology

• Benchmark — 34× in this paper · explained in 2 episodes
• Robustness — 20× in this paper · explained in 5 episodes
• Verification — 14× in this paper · explained in 2 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Quantum Approximate Optimization Algorithm — 10× in this paper
• Differential Evolution — 8× in this paper
• Benchmarking — 6× in this paper · explained in 4 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Congress on Evolutionary Computation — 3× in this paper
• Frustration — 3× in this paper · explained in 2 episodes
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• Hypergraph — 2× in this paper · explained in 2 episodes
• Local Optimization — 2× in this paper · explained in 2 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Hubbard Model — 1× in this paper · explained in 3 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Simultaneous Perturbation Stochastic Approximation — 1× in this paper

Sources

• A Quantum Approximate Optimization Algorithm
• From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning
• Numerical Optimization Strategies for the Variational Hamiltonian Ansatz in Noisy Quantum Environments
• Reliable Optimization Under Noise in Quantum Variational Algorithms
• Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution
• Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise
• Classical Optimization Strategies for Variational Quantum Algorithms: A Systematic Study of Noise Effects and Parameter Efficiency

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