The Applications of Quantum Computing in Robots

This past summer, I studied more about Quantum Computers via UTD's quantum computing research internship. We studied a myriad of topics, ranging from mathematical equations to the fundamentals behind quantum physics. However, the most interesting topic that we covered was the actual applications of quantum computing. This whole time, I kept wondering, "Quantum is cool and all... but what's in it for us?"

After the seminars, I think I have a few answers for myself. But to first understand the difference, we need to consider the differences between quantum and classical computing. Why not stick with what we have? Classical computers are fast, reliable, and have supported decades of innovation. However, they hit a wall when it comes to problems with massive complexity, like simulating molecules, optimizing supply chains, or training powerful AI systems. That’s where quantum computing comes in. By harnessing the quirks of quantum mechanics, such as superposition and entanglement, quantum computers have the potential to solve certain problems exponentially faster than classical machines ever could.

So what are some areas we could use this in?? I think the most classic example of quantum superiority over classical computing is maze solving. The way a classical computer solves it is by moving in a particular direction till it hits a wall, then turning in a random direction and moving on. If it hits a dead end, it backtracks till it finds a new opening and moves down that path. But a quantum computer works differently; it can explore multiple paths simultaneously thanks to superposition. Instead of checking one route at a time, it considers all possible solutions at once and zeroes in on the correct one far more efficiently.

The example I used is trivial; maze solving is no world-changing thing. However, if you look beyond the scope of the "game," you would notice how valuable the ability to consider multiple solutions truly is. This technology could be used in navigation, like for map apps and even for self-driving. Considering the fact that cars need to account for lights, obstacles, road directions, etc, quantum computing would serve as a boon for the AI system to safely navigate its passengers to their destination.

For my capstone project, I decided to create a path-planning system that implemented a quantum state simulator with qubit manipulation. I applied quantum gates (Hadamard) to demonstrate quantum superposition and to simulate quantum measurement with probability distributions. The simulation demonstrated the advantages of quantum computers in applications involving heavy path planning, like seabed exploration, home navigation, GPS services, etc. The program, in particular, was designed to navigate between a series of "blocks" or walls and had to establish path nodes towards the destination.

That’s why I’m excited about quantum computing. It opens up a new way of approaching complex problems, especially in fields I care about like robotics and AI. I see real potential in using it to improve how robots learn, adapt, and make decisions. In college, I want to explore both the theory and the hands-on side of quantum computing, and find ways to connect it with the intelligent systems I build.