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CCF Discoveries

Division of Computing & Communication Foundations (CCF)

 

ITR: Quantum Information Processing with Ultracold Rydberg Atoms

Robin Côté, Edward Eyler, and Phillip Goul

The goal of this research at University of Connecticut was to investigate the feasibility of quantum information processing using ultracold Rydberg atoms, at both theoretical and experimental levels. The short-term goal was to demonstrate that entangled states could be generated with Rydberg atoms, and the longer term goal was to realize a quantum logic gate, namely the phase gate.

By selecting the Rydberg states judiciously, researchers found that it may be possible to obtain a conditional excitation where only one atom can be excited at a time. We showed that this effect, named dipole blockade, could be generalized to an ensemble of atoms, i.e., only one atom can be excited into a Rydberg state in the mesoscopic ensemble. Such an effect would simplify the manipulation of information and could be used to entangle various ensembles.




Adaptive Neurally-Inspired Computing: Models, Algorithms, and Silicon-Based Architectures

Rajesh P. N. Rao and Christopher J. Diorio

The goal of this research at the University of Washington was to discover primitives for adaptive neural computing by simulating biological neurons and synapses, develop algorithms for computation and learning in networks of spiking neurons using these primitives, and implement these algorithms in silicon using synapse transistors and field-programmable learning arrays (FPLAs).

A new learning algorithm was developed based on the recently discovered neurobiological phenomenon known as redistribution of synaptic efficacy. The learning algorithm adapted the input connections to a neuron in such a way that the neuron became selective for specific temporal patterns. This allowed a network of neurons to learn temporal sequences (such as audio or video) without any external supervision.

This research used a Matlab-based simulator for investigating computing and learning in networks of spiking (integrate-and-fire) neurons.




Primate-Inspired Specialized Learning in an Agent Architecture: Safe, Robust, Adaptive Action Selection

Marc D. Hauser (PI) and Joanna Bryson

This pilot study (SGER) by researchers at Harvard University explores whether modeling the ways non-human primates learn can improve safe learning in artificial agents and seeksA photograph of two non-human primates. to better understand the limits of learning that primates (sometimes including humans) have. These limits may actually be useful for agents (both natural and artificial) because they help make learning quick and accurate. They may also safeguard learning from interfering destructively with existing knowledge.

Eight months into the grant, the PIs had five related software-agent workshop publications accepted and an animal-cognition journal article in preparation. Current simulations are developing entirely new models of task-learning in primates. The research has been accelerated by Jonathon Leong, a neuroscience undergraduate, who has been running experiments, and, as a side effect, contributing to the debugging of the software environment being used for building the agents. Producing educational tools is a long-term goal of the project.

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