Quantum AI

Quantum AI is still a young field, far from reaching its full potential. Our research focuses on developing quantum algorithms for applications such as quantum simulation, optimization, machine learning, and linear algebra. We aim to advance methodologies for both contemporary NISQ (Noisy Intermediate-Scale Quantum) devices and future error-corrected quantum processors.

We emphasize leveraging quantum computing to advance AI and machine learning, as many tasks in these domains require solving complex optimization problems or performing efficient sampling. At an algorithmic level, quantum computing replaces classical Boolean logic, enabling quantum-enhanced machine learning. This can lead to faster and more accurate outcomes in areas like quantum neural networks and quantum stochastic control, which have potential applications in drug development, climate simulation, and beyond.