Collaborative: Systems for Quantum Learning: Perceptrons and Networks

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AbstractCollaborative(0202087) David Meyer, UCSD & (0202055) Mitchell Rothstein, U of GeorgiaSystems for Quantum Learning: Perceptrons and NetworksTwo radical ideas have infiltrated computer science and engineering from the natural sciences: that computers might learn, like biological systems, and that computation might he quantum mechanical, like physical systems. These ideas have led to artificial neural networks and machine learning theory, and to quantum computation, respectively. The practical advantages of these two computational models are orthogonal: the former are used to attack practical problems like image classification and control of complex systems for which there are no obvious algorithmic approaches; the latter (putting aside the fact that large scale quantum computers have not yet been built) has a known algorithm for only one specific practical problem-factoring large numbers--but that algorithm runs immensely faster than the best classical algorithm known.The project combines both of these computational models. It will address the possiblility of quantum learning for practical reasons. The ultimate goal is to develop quantum learning models which have strengths analogous to classical ones-the ability to find quantum solutions to problems for which there is no apparent quantum algorithmic solution. Only the most rudimentary steps have been taken in this direction-hence the project will approach this goal from the bottom up. The PI's will investigate a specific architecture (inspired by biology): quantum perceptrons, and more generally, quantum neural networks. Then will carefully define a completely quantum perceptron which can be connected into a network, analyze its capacity, derive learning rules for it, apply these rules to various tasks, and extend each of these objectives to quantum neutral networks. This work will emphasize the importance of designing quantum systems with performance not achievable by corresponding classical systems.