Innovating Three Core Domains with Quantum Computing

Quantum algorithms are expected to offer exponential acceleration in domains where classical methods fall short — such as chemistry, materials science, and CAE (computer-aided engineering) — each of which is home to some of the most pressing challenges across industries.

• Chemistry : Simulating strongly correlated systems, such as the nitrogenase cofactor FeMoco, which are extremely challenging to model accurately using conventional methods.
• Materials Science : Simulating next-generation materials — such as high-temperature superconductors and semiconductors — that play a critical role in both industrial innovation and academic research.
• CAE : Simulating complex phenomena like high-Reynolds-number fluid dynamics — a computationally intensive challenge that demands massive resources.

Each of these domains carries significant potential for industrial impact — yet poses computational challenges that demand immense resources when approached with classical high-performance computing. By leveraging hybrid quantum-classical algorithms and approximation techniques, we are already beginning to generate practical insights and applications–deployable within today’s HPC environments.

World-Class PhDs Driving Frontier Research

In these three key domains, our PhD-level researchers are constantly honing their expertise — developing and validating new algorithms and approaches. By integrating academic knowledge, insights from clients, and a deep understanding of real-world industry challenges, we aim to deliver tangible business impact in the short-to-mid term, while paving the way toward long-term quantum advantage.