Quantum artificial neural networks: architectures and components

It is shown by classical simulation and experimentation that quantum artificial neural networks (QUANNs) are more efficient and in some cases more powerful than classical artificial neural networks (CLANNs) for a variety of experimental tasks. This effect is particularly noticeable with larger and more complex domains. The gain in efficiency is achieved with no generalisation loss in most cases. QUANNs are also more powerful than CLANNs, again for some of the tasks examined, in terms of what the network can learn. What is more, it appears that not all components of a QUANN architecture need to be quantum for these advantages to surface. It is demonstrated that a fully quantum neural network has no advantage over a partly quantum network and may in fact produce worse results. Overall, this work provides a first insight into the expected behaviour of individual components of QUANNs, if and when quantum hardware is ever built, and raises questions about the interface between quantum and classical components of future QUANNs

Quantum-inspired Neural Networks

Quantum computing (physically-based computation founded on quantum-theoretic concepts) is gaining prominence because of recent claims for its massively increased computational efficiency, its potential for bridging brain and mind, and its increasing relevance as computer technology develops into nanotechnology. Its impact on neural information processing has so far been minimal. This paper introduces some basic concepts inspired by quantum theory for use in neural network training and identifies a method inspired by the `many universes' interpretation of quantum behaviour which promises greater efficiency and perhaps solvability of problems currently not amenable to a neural network approach. Instead of one network being trained on many patterns, many single layer networks are trained on one pattern each. The weights of each network are used to derive a quantum network, the weights of which are calculated as a superposition of individual network weights. Two microfeature tasks are used..

Are we properly using our brains in seismic interpretation?

Behind the workstation screen is an extraordinary array of technology to which, quite correctly, we devote considerable attention. But we devote far less to the extraordinary technology in front of the screen—the human brain. Our business performance relies fundamentally on human interpretation of increasingly complex images, yet image interpretation by the human visual processing system is an incredibly complex—and imperfect—task. Seismic interpretation relies on the human view of sophisticated and complex images; we need to improve our human interpretation as much as we seek to improve the images themselves. Knowledge of the way the human visual system works can enhance the way we use our best technology (see also Donnelly, Welland, Cave and Menneer, in press). The results of recent experiments by the authors, designed to address some of the specific issues of seismic data display and interpretation, provide the basis for effectively applying this knowledge

Search efficiency for multiple targets

In most visual search experiments, there is only one possible target object or class of objects. The experiment reported here compares performance in these single-target searches against performance when the target can be either of two different stimuli. The targets used in this experiment were color squares. Results showed that conducting two single-target searches is more efficient than carrying out a dual target search. If visual search is driven by a mental template of the object to be found, then searches for two targets may require a very general template, or a pair of templates that are active simultaneously, which apparently produces less efficient search. Many real world search tasks, such as searches of X-ray images by baggage screeners, require simultaneous search for very different targets (“Find any guns or knives or explosive devices.”). This need for generality could result in search that is less directed and therefore less efficient