We outline a training framework for adaptive quantum networks which aims to simultaneously optimize both topological and numerical structure. The procedure is unique in harnessing a coherent ensemble of discrete topological configurations of neural networks, each of which is formally merged into the appropriate linear state space via superposition. Training is carried out within this coherent state space, allowing for parallel revision of differing topological configurations at each step. The formalism provides quantitative, numerical indications for optimal reconfiguration of network topology. Candidate physical implementations for the model are drawn from recent developments in condensed matter physics and solid-state quantum information processing.

We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolving reticular quantum structures as described in quantum gravity. The main feature of this model is moving from particular neural topologies to a quantum metastructure which embodies many differing topological patterns. Using quantum parallelism, training is possible on superpositions of different network topologies. As a result, not only classical transition functions, but also topology becomes a subject of training. The main feature of our model is that particular neural networks, with different topologies, are quantum states. We consider high-dimensional quantum structures as candidates for implementation of the model.

The complex web of the global information grid will undergo explosive changes over coming decades. As advances in science and technology converge, a myriad array of discoveries in biotechnology, nanotechnology and information technology will produce unpredictable effects that must be accounted for in any estimate of what the world will look like in this future. A strategically important feature of this world will be the emerging trend of information warfare. Though still immature at present day, this trend will become increasingly dominant in the years to come. The information warfare of tomorrow will be radically different from its prototype today. No longer will it be confined to the mainframes of the Internet or to corporate databases: the battleground of the future will draw into its scope the scientific advances being made today in bio and nano technologies. The divisions between man and machine will blur. When networked technologies are ubiquitous, a state-sponsored attack on electronic networks can have far-reaching, and devastating, physical consequences.