2 papers: Quantum Dot Neural Network / Optical Neural Network

[JIM STECK]

An uncompressed  postscript version of the following paper is available at: 

http://www.me.twsu.edu/me/faculty/steck/Pubs/   (approx 1400K)


			A Quantum Dot Neural Network

	     E.C. Behrman,  J. Niemel, J. E. Steck, S. R. Skinner
		 Wichita State University, Wichita, KS  67260


				  Abstract

We present a mathematical implementation of a quantum mechanical artificial
neural network, in the quasi-continuum regime, using the nonlinearity
inherent in the real-time propagation of a quantum system coupled to its
environment.  Our model is that of a quantum dot molecule coupled to the
substrate lattice through optical phonons, and subject to a time-varying
external field.  Using discretized Feynman path integrals, we find that the
real time evolution of the system can be put into a form which resembles
the equations for the virtual neuron activation levels of an artificial
neural network. The timeline discretization points serve as virtual
neurons.  We then train the network using a simple gradient descent
algorithm, and find it is possible in some regions of the phase space to
perform any desired classical logic gate.  Because the network is quantum
mechanical we can also train purely quantum gates such as a phase shift.

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An uncompressed  postscript version of the following paper is available at: 

http://www.me.twsu.edu/me/faculty/steck/Pubs/   (approx 153K)

Experimental Demonstration of On-Line Training for an Optical  Neural Network 
Using Self-Lensing Media

Alvaro A. Cruz-Cabrera, James E. Steck, Elizabeth C. Behrman, 
Steven R. Skinner

                                   Abstract

	The optical bench realization of a feed forward optical neural
network, developed by the authors, is presented.  The network uses a
thermal nonlinear material that modulates the phase front of a forward
propagating HeNe beam by dynamically altering the index of refraction
profile of the material.  The index of refraction cross-section of the
nonlinear material was modified by applying a separate argon laser, which
was modulated by a liquid crystal display used as a spatial light
modulator.  On-line training of the network was accomplished by using a
reinforcement learning paradigm to achieve several standard and
non-standard logic gates.


James E. Steck
Assistant Professor
(316)-689-3402