Quantum Bayesian Networks

February 22, 2009

An Elevator Pitch For Quantum Bayesian Networks

Filed under: Uncategorized — rrtucci @ 1:21 pm

salesmanIf you look at the history of computer science (those who ignore history are condemned to repeat its mistakes), you will find that the representation of classical uncertainty in computer programming went through a lot of false starts, but has finally settled into the use of Bayesian networks. Researchers tried to extend rule based systems, functional languages like Lisp, declarative languages like Prolog, etc. to include probabilities, and always ended up with an ugly kludge. Bayesian networks has emerged as the clear winner for representing classical probabilities and uncertainty in computer programs. If you don’t believe me, ask any programmer at Google, or Microsoft (both companies use Bayesian networks extensively), or any person in AI or bio-informatics, or a Bayesian Statistician, etc. Quantum Physics and Probability Theory have much in common. Frequently, they are parallel theories. It would be inexplicably odd if Bayesian networks were not as important to quantum computer programing as they are to classical computer programming. That is why I wrote my Mac application “Quantum Fog” which generalizes Bayesian Networks to quantum mechanics in a minimal way. Bayesian networks have already been successful in illuminating quantum mechanics: for example, they led me to invent squashed entanglement, a new measure of quantum entanglement.

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