aiqbench analysis
Quemix and Nissan have demonstrated a hybrid quantum-classical algorithm that matches the accuracy of classical Lattice Boltzmann Method simulations for complex vehicle geometries, validating a strategy where classical systems handle motion while quantum processors focus on core fluid dynamics. This approach specifically addresses historical limitations in representing curved surfaces on quantum circuits, targeting execution on Early Fault-Tolerant Quantum Computers (Early-FTQC). The validated method offers a pathway to improve aerodynamic analysis across automotive, aerospace, and marine sectors once fault-tolerant hardware becomes available.
Hybrid Quantum-Classical Algorithm Matches Classical Aerodynamic Results

Quemix and Nissan have jointly developed a hybrid quantum-classical algorithm for aerodynamic simulation, targeting improved vehicle performance and efficiency. The technology aims to reproduce classical aerodynamic analysis results, even with the complex geometries of vehicle bodies, using quantum computing.
Quemix and Nissan confirmed June 1 the successful development of a hybrid quantum-classical algorithm capable of reproducing aerodynamic simulation results with accuracy comparable to that of conventional classical computer analysis. Currently, the Lattice Boltzmann Method (LBM) serves as the mainstream simulation technique for analyzing airflow around vehicles; this new approach aims to improve upon that established method. The companies developed an algorithm…
Excerpt shown under the publisher's RSS terms · 62 words · full text and figures are on the publisher's site
Related coverage

News
Quantum Computing Report · Jun 7, 2026

The Companies Building Quantum Computing Chips in 2026
The Quantum Insider · Jun 5, 2026

Lattice-Based Cryptography Explained
Quantum Zeitgeist · Aug 15, 2026
Running Variational Quantum Eigensolver with Qiskit Aer on AMD Instinct
AMD · May 29, 2026