Quantum Annealing

Quantum annealing encodes a problem as an Ising energy function or quadratic unconstrained binary optimization (QUBO), then uses an annealer to sample low-energy configurations. Researchers apply it to combinatorial tasks such as routing and feature selection, as well as machine-learning methods that use Ising models for sampling, attention, or generative learning. Common approaches combine annealer samples with classical preprocessing, optimization, or post-processing, and compare results with methods such as simulated annealing, Gibbs sampling, and exact solvers. A central challenge is whether hardware limits—including embedding overhead, coefficient precision, and constraint satisfaction—allow reliable gains over strong classical baselines on practical problem sizes.