Quantum algorithms and Quantum Machine Learning

Our research focuses on harnessing the potential of quantum computing for algorithmic applications, particularly in areas where a quantum advantage is established or within reach. We concentrate on the NISQ (Noisy Intermediate-Scale Quantum) regime, aiming to bridge theoretical models with practical implementations.

Ongoing research directions include:

• Quantum simulation, with a strong focus on chemistry and materials science problems;

• Quantum machine learning, especially in physics-informed and data-driven applications.

While much of our work is theoretical, we actively test and benchmark quantum algorithms on real hardware, using IBM Quantum devices as well as our own in-house quantum computing infrastructure. This integrated approach helps us close the gap between theory and experiment, paving the way for real-world quantum applications.