The race to develop the first fault-tolerant quantum computer is already underway, but until we reach this goal, small quantum processors that can do some calculations faster than the best available supercomputers are being developed. We are committed to developing algorithms that push the limits of existing quantum hardware to solve important problems already today. For that, we combine our expertise in quantum information with the best available classical strategies, such as network medicine and AI.
Computer-aided drug design techniques have hit the limits of classical computation, and are thus forced to rely on very heuristic techniques with limited predictive power. Emerging quantum technologies will soon become powerful tools in drug development, with the ability to solve complex optimisation problems.
Our approach aims at radically improving both the development time and cost of drug development by skilfully combining the power of quantum algorithms, network medicine and AI for structure prediction, molecular similarity simulation, and the realistic modelisation of protein-ligand docking.
Our quantum chemistry platform Aurora sits on top of leading vendors. It's hardware agnostic, which means they can run on any quantum device, regardless of how it is built. By focusing on the so-called electronic structure problem, to which much of the world's supercomputing efforts are devoted, Aurora can solve the common bottleneck in computer-aided drug design and other problems in life sciences. In addition, Aurora is based on the best IP-protected variational algorithms, which improve on the widely used variational quantum eigensolver.