Jon Rowe

Learn More
In this paper, we will use a quantum operator which performs the inversion about the mean operation only on a subspace of the system (Partial Diffusion Operator) to propose a quantum search algorithm runs in O(N/M) for searching unstructured list of size N with M matches such that, 1 ≤ M ≤ N. We will show that the performance of the algorithm is more(More)
— The bacterium Escherichia coli has the capacity to respond to a wide range of environmental inputs, which have the potential to change suddenly and rapidly. Although the functions of many of its signal transduction and gene regulation networks have been identified, E.Coli's capacity for perceptual categorization, especially for discrimination between(More)
Using a simple simulation model of evolution and learning, this paper provides an evolutionary argument why Lamarckian inheritance the direct transfer of lifetime learning from parent to offspring-may be so rare in nature. Lamarckian inheritance allows quicker genetic adaptation to new environmental conditions than non-lamarckian inheritance. While this may(More)
In this paper we will present a quantum algorithm which works very efficiently in case of multiple matches within the search space and in the case of few matches, the algorithm performs classically. This allows us to propose a hybrid quantum search engine that integrates Grover's algorithm and the proposed algorithm here to have general performance better(More)
In this paper, we will define a quantum operator that performs the inversion about the mean only on a subspace of the system (Partial Diffusion Operator). This operator is used in a quantum search algorithm that runs in O(N/M) for searching an unstructured list of size N with M matches such that 1 ≤ M ≤ N. We will show that the performance of the algorithm(More)