This week, I completed the course QIS102: Applied Quantum Computing. We finished up the course discussing Simulation and Modelling, Differential Equations, Monte Carlo Methods, Digital Circuits, Quantum Mechanics, and Quantum Circuits. Finally, we concluded with a discussion of various Quantum Algorithms present in theory and in practice today.

I began generating circuits in Qiskit, as well as evaluating them with various backends and generating histograms to visualize results. This code can be found in the “qiskit_tasks” folder in my QIS102 Repository on my GitHub. I enjoyed being able to officially practice the skills I had previously learned through IBM’s quantum learning tutorials, and look forward to continuing to increase my familiarity with Qiskit.

My project scope has continued to evolve, but I am still looking forward to figuring out ways to utilize GPU capabilites to improve the speed, as well as memory capabilties, of classical reconstruction. Next week, I look forward to being able to dive fully into researching this project.

For our research paper review group this week, we read “Architecting Noisy Intermediate-Scale Trapped Ion Quantum Computers” by Prakash Murali and colleagues. This paper examines the design considerations for ion trap quantum computers, particularly focusing on the Quantum Charge Coupled Device (QCCD) architecture. It highlights the trade-offs between different trap sizes, chip geometries, and gate implementations, and emphasizes the importance of choosing optimal configurations based on algorithm connectivity while consistently favoring gate-based swaps over physical ion swaps for better performance.