Function Maximization with Dynamic Quantum Search

Finding the maximum value of a function in a dynamic model plays an important role in many application settings, including discrete optimization in the presence of hard constraints. We present an iterative quantum algorithm for finding the maximum value of a function in which prior search results update the acceptable response. Our approach is based on quantum search and utilizes a dynamic oracle function to mark items in a specified input set. As a realization of function optimization, we verify the correctness of the algorithm using numerical simulations of quantum circuits for the Knapsack problem. Our simulations make use of an explicit oracle function based on arithmetic operations and a comparator subroutine, and we verify these implementations using numerical simulations up to 30 qubits.</p

Contextually Aware Information Delivery in Pervasive Computing Environments.

This paper outlines work in progress related to the construction of a system which is able to deliver information in a contextually sensitive manner within a pervasive computing environment, through the use of semantic and knowledge technologies. Our approach involves modelling of task and domain as well as location and device. We discuss ideas and steps already taken in the development of prototype components, and outline our future work in this area. © Springer-Verlag Berlin Heidelberg 2005