Quantum Hierarchical (multilevel) Modeling

Quantum hierarchical (multilevel) modeling can be used for learning many related models, accomplished by exploiting the excited states of quantum systems.  Quantum computers have the potential to solve a number of difficult machine learning problems such as factoring and unstructured search much more efficiently than their classical counterparts. 


This can be accomplished by encoding and processing of quantum information in ensembles of multilevel quantum systems.  This power stems mainly from the quantum superposition principle, which allows a single system to simultaneously explore the entire (computational) state space. 

"Our responsibility is to do what we can, learn what we can, improve the solutions, and pass them on."

Richard P. Feynman

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