Godin Ryan
Project
Quantum data encoding in bioinformaticsApplication of quantum algorithms requires unique quantum data encoding, taking classical data and converting them into quantum states. However, encoding classical data requires appropriate representation to ensure that information needed for a specific task is not lost. The quantum Hilbert space allows the expression of data in a higher dimension, increasing the expressivity between features, which is particularly useful for classically hard data such as hyperspectral images. In addition, quantum data encoding allows the development of quantum algorithms that can be more efficient than their classical counterpart. Development of novel encoding techniques in phylogenetics can enable more efficient distance metrics calculation for clustering tasks. In this master project, we plan to demonstrate both novel and ideal encoding methods for both hyperspectral data and phylogenetic trees which are important data types within bioinformatics. To validate the effectiveness of these encoding methods, each is passed through a respective quantum algorithm and compared to their classical counterparts. We demonstrate both techniques ability to accurately capture the important features of both types of data.
