We’re excited to share our latest project in the Quantum Computing Club — a deep dive into Quantum Reinforcement Learning (QRL) for traffic signal optimization! Imagine a world where traffic signals aren’t just following pre-set timers, but actively adapting to traffic in real time, reducing congestion, saving energy, and making city life smoother. Wouldn't that be awesome?! Well, in the Quantum Computing Club, that’s exactly what we’re working toward!
What We’re Doing
Traffic congestion is a major challenge in cities worldwide. Traditional methods of optimizing traffic signals often fall short, especially as cities grow. We’re exploring how Quantum Computing and Reinforcement Learning can work together to make traffic systems smarter and more efficient.
Our approach:
Use Quantum Optimization approaches like Quantum Annealing and QAOA to solve the traffic control problem
Train and test Quantum Reinforcement Learning (QRL) agent(s) to learn and adapt to real-world traffic patterns
Scale algorithms to various real-world traffic intersections and analyze their performance
Stay tuned to see what we discover!!