Six Japanese firms (TOPPAN, AISIN, Asahi Kasei, SoftBank, AGC, DIC) to build quantum‑computing materials platform
- Project selected by NEDO under the Enhanced Infrastructures for Post‑5G information and communication systems call.
- R&D will run from fiscal 2026 through fiscal 2028 to construct a next‑generation materials development platform.
- The platform will integrate quantum‑computing calculations with AI and classical computing to mitigate precision, scale and cost limits.
- Verification targets include next‑generation batteries, optical functional materials, superconductors, polymers, optical glass, colorants and semiconductor materials, with each company testing its domain.
Project scope and timeline
Six companies jointly received selection by NEDO under its Research and Development Project of the Enhanced Infrastructures for Post‑5G Information and Communication Systems. They will run a joint R&D program from fiscal 2026 through fiscal 2028 to build a next‑generation materials development platform that bridges demonstration and industrial application.
Technical approach
The effort will combine quantum‑computing‑based calculations with AI (including machine learning) and classical supercomputing to expand computational accuracy and scale while reducing cost barriers. The partners note current quantum hardware limits—qubit counts and error rates—so the platform will use hybrid methods rather than relying on quantum computers alone.
Verification and company roles
Verification will be cross‑sectoral: next‑generation batteries, optical functional materials for displays and sensors, superconducting materials, organic electrolytes and polymers, inorganic electrodes and optical glass, colorants and semiconductor materials. Each company will apply and validate methods in its domain; partners will also collaborate with specialized quantum institutions and use experiment feedback to improve predictive models.
Expected output
The outcome is a continuously evolving materials development platform that integrates quantum, simulation and data resources to improve prediction accuracy and accelerate material discovery and design for industrial application.
Source: Asahi Kasei