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