自動車

Thermal management is a critical challenge in electric vehicle (EV) design, directly impacting performance, safety, and battery longevity. Simulating cooling systems such as heat flow through or between components requires solving complex equations which can become computationally expensive with classical tools.

In a joint use-case with the BMW Group, Classiq explored how quantum computing could support the simulation of EV cooling systems. The project focused on modeling and optimizing heat transfer in tightly coupled mechanical and electrical subsystems using quantum algorithms designed to reduce computational overhead.

By expressing high-level thermal models and automatically generating optimized quantum circuits, the PoC demonstrated the potential for more scalable simulation workflows. Quantum-enhanced approaches were assessed for their ability to improve modeling fidelity and accelerate numerical solutions.
This collaboration demostrated how quantum computing can complement classical simulation techniques in automotive R&D, exploring a pathway to more efficient thermal design and accelerated innovation in next-generation electric vehicles.

Designing advanced battery materials such as solid electrolytes, high-capacity cathodes, or fast-charging anodes requires accurate modeling of atomic-scale properties like ion mobility, phase stability, and electron transport. Classical methods often struggle with the quantum complexity of these systems, especially when dealing with strongly correlated materials.

A quantum computing project in battery material optimization explores how quantum algorithms, particularly the Variational Quantum Eigensolver (VQE) or Quantum Phase Estimation (QPE), can simulate the electronic structure of novel compounds with higher fidelity. This enables better screening of candidate materials for properties like conductivity, voltage stability, or degradation resistance.
The use-case typically focuses on representative molecular fragments or crystalline structures, benchmarking quantum-derived insights against classical computational chemistry methods such as DFT. By modeling energetics, charge distribution, or diffusion pathways, the study may inform which materials might offer superior performance.

For energy storage innovators, this approach offers a potential leap forward in accelerating the discovery of safer, more efficient, and longer-lasting battery chemistries vital to the future of electric vehicles, grid storage, and portable electronics.