Ali Implements Quantum Error Correcting Codes on the IBM Melbourne Quantum Computer

Ali is a student from the United States who attended the Summer STEM Institute (SSI). Before SSI, Ali conducted several machine learning and computer vision projects, from helping doctors detect nuclei for disease detection to predicting droughts using satellite data. Over the summer, Ali explored his interests in quantum computing through his project titled, “Implementing Quantum Error Correcting Codes on the IBM Melbourne Quantum Computer,” where he implemented Shor’s algorithm to correct arbitrary single qubit errors in quantum computers. You can learn more about his research project and find his full paper here. Next fall, Ali will be attending Stanford University where he is excited to continue pursuing his passion for quantum computing and machine learning.

How were you first introduced to the world of scientific research, and what previous projects have you worked on? 

In my sophomore year of high school, I took this class called Honors Science Research. Basically, you get matched with a mentor and conduct an independent study project by yourself. I took that class without really knowing what science research was or how to conduct it. I just kind of learned along the way. I really wanted to work on a machine learning project, so I took a class at Stanford called CS 231N: Convolutional Neural Networks for Visual Recognition. I learned about convolutional neural networks and worked on a project to help doctors more efficiently use images of nuclei to identify whether or not someone has a disease. Currently, doctors have to spend hours manually identifying where the nucleus of the cell is. My project used a convolutional neural network to automate that process and automatically detect where the nuclei were. Then, in my junior year of high school, I conducted another project using convolutional neural networks to predict droughts using satellite images. 

Outside of conducting machine learning research, what other extracurricular activities are you involved in? 

I play three sports at my high school: cross country, squash, and track and field. I’ve been on those sports teams for all four years. Also, I’m the president of the diversity club at my school. Every week, we meet and discuss issues related to race or identity. It’s kind of the opposite of my science proceeds, but the club gives me the opportunity to kind of take a break from science and focus on something else. 

Outside of school, I’m really into reading. I picked it up over quarantine, and I’ve just been reading a lot, which has been really fun. I just finished this book called The Infinite Powers that theorizes calculus in an intuitive way. It talked about how calculus can be made simple and be taught more effectively. I also recently started this book called Nine Algorithms that Changed the World that I’m also really excited about. It talks about computer science algorithms we interact with every day without even realizing they make up the foundations of modern-day computing. 

Also, I’m big into cooking and baking. I like baking any type of bread, from banana bread to orange bread. I’m from Egypt, so my mom has also taught me to cook a lot of Egyptian dishes. I especially enjoy cooking koshari. 

What areas of science or technology are you most interested in? 

I’m really into both physics and computer science. My SSI research project combined these two elements since it was related to both quantum mechanics and computer science. I first became interested in quantum mechanics when I participated in a summer program at Stanford called the Stanford Pre-Collegiate Summer Institute. I’m really excited about quantum computers in general and how all these companies are competing to create the best quantum computers. There are so many applications of quantum computers that extend beyond physics. For example, the fields of medicine and finance can be greatly accelerated by quantum computing. 

Can you summarize the research project you worked on at SSI in layman's-terms? 

Quantum computers can run these massive algorithms very, very quickly. However, they are very prone to error because the quantum computers themselves have to be run in very cold conditions. If the temperature is off, or if the person manning the machine messes up, you have to be able to correct these errors. My project was basically implementing an algorithm that could correct the errors that quantum computers make on a real quantum computer in Melbourne, Australia, called the IBM Melbourne.

That sounds fascinating. How did you come up with the idea for your project? 

Like I said earlier, I’m really into both quantum mechanics and computer science. My mentor introduced me to this whole field of quantum computing, and he pushed me to read quantum survey papers talking about the recent developments in the field. I realized that a lot of the algorithms that people were producing that could correct the error were mainly theoretical. No one had actually written the software to implement many of these algorithms. I looked at one algorithm called the Shor Code, which was written by Peter Shor, a professor at MIT. He invented error correcting code that no one had implemented before on a large quantum computer, so that’s where I decided to go with my project. 

What were the most valuable lessons or skills you learned from completing your project? 

I’d say there were two main lessons. First, I learned how to write a paper and use LaTeX, which I had never done before. I learned how to write a good abstract and a good conclusion, which is probably one of the most valuable skills I learned over the summer. Also, I learned how to conduct research in general. I never really learned how to look at survey papers, study what others had done, or develop my own ideas in a formulated way. It was pretty cool to learn the structure and process of conducting research. 

What was the most challenging part of conducting your research? How did you overcome it? 

In quantum computing, you must have a lot of background knowledge and a strong foundation in quantum mechanics. I forgot some of the concepts I learned the previous summer, so I had to read a lot of background research and review concepts in linear algebra and tensors. It was pretty tough because it was a lot of high-level information condensed into the first week or so. I was able to overcome the challenge because I worked pretty closely with my mentor. He was very helpful and was always available to talk things through with me. He was always there to just check over my solutions or explain things really well. 

What was your favorite part of the research process, and what is the most interesting or unexpected thing you learned when completing your project? 

I'd say my favorite part of the process was getting results. The quantum computer I was using was actually shut down for a week and a half during the program because they were undergoing maintenance. I basically had to put my research on halt because I couldn’t access the computer. I spent that time cleaning up my code and making sure everything would run perfectly when the computer got back up and running. After the computer was done with maintenance, I just ran all the code and everything worked. It was really satisfying. Overall, I learned how to face adversity in research because obviously there’s always going to be times when things don’t go your way. You just have to learn how to deal with it, and I was able to experience this firsthand. 

What was the highlight of your summer? 

My final presentation was probably my favorite part of the summer because I got to highlight all the work I’ve been doing. When the other mentors asked questions, I was able to answer clearly. It just felt like all the hard work I put in was paying off because I could just speak confidently about my project in a way that everyone could understand. I was able to simplify concepts and really explain my project, which was really satisfying. 

What have you been up to after SSI? Are there any new projects or research you’ve been working on that you’re excited about? 

Right now, I’m working on another machine learning project. My friends and I are making an armband that uses your muscle signals to translate sign language. We’re very close to getting results, which is super exciting. We’re planning on publishing a paper on our work soon, and then after that, we are planning to commercialize the technology and patent it.