Application
Particle Track Pattern Recognition via Content Addressable Memory and Adiabatic Quantum Optimization

In the applied physics lab at Johns Hopkins, researchers are leveraging quantum annealing for pattern recognition in high energy physics particle detection. Quantum annealing enables more accurate pattern matching and access to a family of low-energy solutions that improve track reconstruction.

INDUSTRY : Physical Sciences
DISCIPLINE : Machine Learning