This week, I continued to work towards my Applied Quantum Computing certificate. We have recently discussed complex algebra, numerical analysis, classical wave equations, and Fourier analysis; all tools essential to understanding and performing quantum operations. I have continued to produce small Python programs that solidify these concepts, which can be found in my GitHub repository entitled QIS102.

As my project scope has continued to evolve, I have continued to research different ways to contribute to the CutQC repository. I have decided to utilize GPU capabilites to improve the speed, as well as memory capabilties, of the classical reconstruction step in order to expand the available use of CutQC to larger quantum systems.

To do this, I have worked to increase my knowledge of CUDA, NVIDIA’s parallel computing platform and programming model for GPUs. I have also worked to connect to Princeton University’s research computing clusters, to prepare for testing my GPU code on output distributions from the subcircuits produced by CutQC’s code.