The automotive industry is lined up to be a very strong early adopter of quantum technology for a variety of reasons. Quantum computers are particularly good at optimization and simulation. For automobiles, this includes solving complex challenges in material design, battery technology simulation, and eliminating costly and time-consuming real-time testing and prototyping wherever possible.
Ford and BMW recently announced how they are using the Quantinuum InQuanto computational chemistry software platform to meet the challenge.
Ford and Battery Chemistry
Quantum researchers at Ford, for example, recently published new results using quantum computers with Quantinuum model EV battery materials, promising that more powerful systems of the future will enable valuable chemical simulations. Proven. Ford’s research team used Quantinuum’s InQuanto in combination with the company’s H-series ion trapping quantum hardware to test simulations of lithium-ion battery chemistries.
The challenge is that while lithium-ion batteries can be charged and discharged many times, they are sensitive to heat and are inherently flammable. Improvements in energy density, power density, life cycle, safety, cost and recyclability are all in the planning stages. This is where quantum computational chemistry comes into play. According to the study, “computational chemistry can provide insight into charge/discharge mechanisms, electrochemical and thermal stability, structural phase transitions, and surface behavior, and plays an important role in finding potential materials.” It can improve battery performance and robustness.”
When using quantum computers to study the chemistry of lithium-ion batteries, scientists used algorithms to find the ground state of a quantum mechanical system. Hybrid quantum-classical algorithms solve the parts of molecular systems that benefit most from quantum computation, and the rest of the computation is directed to classical computers.
BMW and hydrogen fuel cells
BMW announced that it is using AWS’s InQuanto platform to simulate the surface properties of materials used in hydrogen fuel cell powertrains. A major challenge in developing new fuel cell technologies is the slow rate of the oxygen reduction reaction (ORR). Most studies involving catalytic and electrocatalytic chemical reactions such as ORR use density functional theory (DFT) methods of computational chemistry. DFT relies on error cancellation, which is insufficient accuracy for this application. However, quantum computing has the potential to provide accurate computation of complex systems without the DFT compromises.
The InQuanto platform frees computational chemists to focus on their research using proven codes and algorithms available from the InQuanto library, rather than writing a lot of code. Computational chemists new to quantum systems have access to an easy-to-use interface. They solve scaled molecular and materials problems on quantum hardware, including the IBM series of superconducting circuit devices, Quantinuum’s H series of ion trap devices, Powered by Honeywell, and various other hardware devices and emulators. can be simulated.
inQuanto 2.0 has just been released to provide users new to quantum computers with a more versatile, extensible, and more applicable platform. InQuanto is built around state-of-the-art quantum algorithms, advanced subroutines, and chemistry-specific noise reduction techniques. The new version improves efficiency with a new protocol class that speeds up vector computations by orders of magnitude, and an integral operator class that can take advantage of symmetry to reduce memory requirements. Find out more at Ford, BMW and InQuanto.
December 22, 2022