During my time at QCI, I contributed to a diverse range of projects that significantly enhanced my expertise in quantum computing and software engineering. From developing quantum solutions for complex optimization problems to building scalable web applications, my time at QCI was both challenging and rewarding. Below is an overview of some key projects and the skills I developed.

Quantum Formulations

One of my primary projects involved developing quantum formulations for the Travelling Salesperson Problem (TSP), a widely recognized NP-hard problem in logistics. Traditionally solved by supercomputers like NEOS ↗ , I worked on translating a linear programming model into quantum-friendly matrices and vectors. This formulation, initially limited to a fixed number of locations, evolved to handle varying locations, weights, capacities, and demand.

The quantum-powered solution was integrated into a Streamlit web application, enabling users to input real-world traffic data and calculate the shortest path, while also providing suboptimal yet sufficiently distinct alternatives. This software became a demonstration tool for QCI to showcase the unique capabilities of quantum computing to potential clients.

Los Alamos National Laboratory (LANL) Collaboration

In collaboration with LANL, I contributed to the development of neighborhood detection and graph partitioning algorithms using quantum techniques. Leveraging research from the University of Minnesota, my role was to design a recursive quantum algorithm that identified optimal points for splitting graphs and uncovering connections [1]. Though this was a brief engagement, it offered valuable exposure to quantum research and algorithm development.

Qatalog Platform

After completing the Quantum TSP project, I was tasked with building a web application for QCI’s quantum software platform, aimed at streamlining collaboration between the company’s mathematical, software, and business teams. Using Django with a REST API and RDS, I developed a modular, scalable application that allowed researchers to upload quantum formulations, which were then translated into algorithms by software engineers and utilized by the business team for client solutions. Deployed on AWS Elastic Beanstalk, this platform became a crucial tool for advancing quantum algorithm development at QCI.

Quantum Machine Learning

Another significant project was the development of a quantum machine learning algorithm to classify the MNIST dataset using TensorFlow Quantum. I created a testing suite to evaluate the accuracy and performance of different quantum machine learning models, with the results serving as foundational documentation for future projects.

QUBT-U Initiative

Towards the end of my time at QCI, I was appointed as a core course developer for QUBT-U, an educational initiative designed to introduce quantum computing to universities across the country. My role involved creating course content that demonstrated how to leverage QCI’s quantum products for various real-world applications, from optimization problems to game development.

This experience at QCI allowed me to gain hands-on knowledge in quantum computing while contributing to innovative projects that demonstrated the practical applications of quantum technologies in both industry and education.

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