Total explores quantum algorithms to improve CO2 capture
Improving quantum algorithms for quantum chemistry |
Total explores quantum algorithms to improve CO2 capture
Total, which ranks eleventh in the ranking of the most powerful machines in the world after the inauguration of its supercomputer Pangea III, is mobilizing on the field of quantum algorithms by signing a multi-year partnership with the start-upup English Cambridge Quantum Computing. This agreement aims to improve the materials used for CO2 capture. Total indicated that it would like to invest up to 10% of its annual research and development effort in this area.
The group is working on nanoporous (absorbent) materials as part of its research. «The quantum algorithms, which will be developed within the collaboration between Total and CQC, will simulate all the physical and chemical mechanisms in these absorbents according to their size, their shape, their chemical make-up and will thus enable the selection of the most efficient materials to be developed,” says Total. However, such simulations are currently impossible to achieve with a «conventional supercomputer», justifies the oil group.
Co2 Capture Materials
Total claims a pioneering spirit in this area. “Quantum computing opens up new possibilities for solving extremely complex problems,” says Marie-NoĆ«lle Semeria, Total’s Group R&D Director. “We are among the first to use quantum computing in our research to design new materials that can capture CO2 more efficiently,” she continues. “
Total has a long-term commitment to CCUS solutions. We hope that our collaboration will lead to meaningful contributions and accelerated progress towards carbon neutrality,” said Ilyas Khan, CEO of Cambridge Quantum Computing.
Last year, Atos delivered its Quantum Learning Machine simulator to the oil group for cross-functional applications in molecular chemistry and materials, the optimization of energy networks, vehicle fleets, and industrial and longer-term tools, seismic imaging or fluid mechanics, the NSE was mentioned.