Capgemini’s Quantum Lab’s QDDRD is a novel end-to-end concept for quantum chemistry and material science. Machine learning is already beneficial in R&D; however, it often fails to address applications that emerge from quantum mechanical effects. Strongly-correlated many-electron effects, particularly, are notoriously difficult to simulate classically but a natural fit for quantum computers and a promising avenue for quantum advantage. We propose a data-driven approach based on computed quantum features to combine the best of both worlds. We generate features at small scales through appropriate embeddings for downstream predictions within a flexible and scalable data-driven pipeline. We invite clients and partners to collaborate with us on the methodology and accelerate an applied quantum advantage in material science and quantum chemistry.

Julian van Velzen, Head of Capgemini’s Quantum Lab, Capgemini