It has been proven that universal quantum computers based on qubits and classical analog networks both have superTuring capabilities. It is a grand challenge to computer science to prove that the combination of the two, in analog (continuous variable) quantum computing, offers supersuperTuring capability, the best we can achieve. Computing with continuous spins is now the most promising path AQC. Two papers at SPIE2014 described unbreakable quantum codes using continuous spins beyond what traditional qubits allow. To make this real, we must first develop a realistic ability to model and predict the behavior of networks of spin gates which act in part as polarizers. Last year I proposed a triphoton experiment, where three entangled photons go to linear polarizers set to angles θa, θb and θc. Assuming a “collapse of the wave function” yields predictions for the coincidence detection rate, R3/R0(θa, θb, θc) significantly different from the prediction of a new family of models based on classical Markov Random Fields (MRF) across space time, even though both yield the same correct prediction in the two-photon case. We cannot expect to predict systems of 100 entangled photons correctly if we cannot even predict three yet. Yanhua Shih is currently performing this experiment, as a first step to demonstrating a new technology to produce 100 entangled photons (collaborating with Scully) and understanding larger systems. I have also developed continuous-time versions of the MRF models and of “collapse of the wave function”, so as to eliminate the need to assume metaphysical observers in general.
This paper gives highlights of the history of the neural network field, stressing the fundamental ideas which have been in play. Early neural network research was motivated mainly by the goals of artificial intelligence (AI) and of functional neuroscience (biological intelligence, BI), but the field almost died due to frustrations articulated in the famous book Perceptrons by Minsky and Papert. When I found a way to overcome the difficulties by 1974, the community mindset was very resistant to change; it was not until 1987/1988 that the field was reborn in a spectacular way, leading to the organized communities now in place. Even then, it took many more years to establish crossdisciplinary research in the types of mathematical neural networks needed to really understand the kind of intelligence we see in the brain, and to address the most demanding engineering applications. Only through a new (albeit short-lived) funding initiative, funding crossdisciplinary teams of systems engineers and neuroscientists, were we able to fund the critical empirical demonstrations which put our old basic principle of “deep learning” firmly on the map in computer science. Progress has rightly been inhibited at times by legitimate concerns about the “Terminator threat” and other possible abuses of technology. This year, at SPIE, in the quantum computing track, we outline the next stage ahead of us in breaking out of the box, again and again, and rising to fundamental challenges and opportunities still ahead of us.
Is it possible for a simple lumped parameter model of a circuit to yield correct quantum mechanical predictions of its behavior, when there is quantum entanglement between components of that circuit? This paper shows that it is possible in a simple but important example – the circuit of the original Bell’s Theorem experiments, for ideal polarizers. Correct predictions emerge from two alternative simple models, based on classical Markov Random Fields (MRF) across spacetime. Exact agreement with quantum mechanics does not violate Bell’s Theorem itself, because the interplay between initial and final outcomes in these calculations does not meet the classical definition of time-forwards causality. Both models raise interesting questions for future research. The final section discusses several possible directions for following up on these results, both in lumped system modeling and in more formal and general approaches. It describes how a new triphoton experiment, not yet performed, may be able to discriminate between MRF models and the usual measurement formalism of Copenhagen quantum mechanics.
Classical adaptive control proves total-system stability for control of linear plants, but only for plants meeting very restrictive assumptions. Approximate Dynamic Programming (ADP) has the potential, in principle, to ensure stability without such tight restrictions. It also offers nonlinear and neural extensions for optimal control, with empirically supported links to what is seen in the brain. However, the relevant ADP methods in use today--TD, HDP, DHP, GDHP--and the Galerkin-based versions of these all have serious limitations when used here as parallel distributed real-time learning systems; either they do not possess quadratic unconditional stability (to be defined) or they lead to incorrect results in the stochastic case. (ADAC or Q- learning designs do not help.) After explaining these conclusions, this paper describes new ADP designs which overcome these limitations. It also addresses the Generalized Moving Target problem, a common family of static optimization problems, and describes a way to stabilize large-scale economic equilibrium models, such as the old long-term energy mode of DOE.
Conference Committee Involvement (5)
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
19 April 2006 | Orlando (Kissimmee), Florida, United States
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III
30 March 2005 | Orlando, Florida, United States
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II
14 April 2004 | Orlando, Florida, United States
Independent Component Analyses, Wavelets, and Neural Networks
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.