Six Japanese companies launch joint R&D on a quantum-computing materials development platform, backed by NEDO
TOPPAN, Aisin, Asahi Kasei, SoftBank, AGC and DIC will spend fiscal 2026 to fiscal 2028 building a materials development platform that pairs quantum computing with AI.
- 6
- Fiscal 2026 to fiscal 2028
- March 31, 2027
What Happened
In a release dated Tokyo, October 28, 2026, TOPPAN Holdings, AISIN, Asahi Kasei, SoftBank Corp., AGC and DIC jointly announced that their shared proposal, Research and Development of Innovative Materials Development Platforms Using Quantum Technology, has been selected by Japan's New Energy and Industrial Technology Development Organization (NEDO). The selection came under NEDO's solicitation for the Research and Development Project of the Enhanced Infrastructures for Post-5G Information and Communication Systems. Following the selection, the six companies signed a joint agreement to carry out the R&D work from fiscal 2026, the year ending March 31, 2027, through fiscal 2028. The stated aim is to build a next-generation materials development platform that combines quantum-computing calculations with AI technologies such as machine learning, closing the gap between demonstration and real-world application.
The companies explain that data-driven methods, in which AI predicts and explores new materials from historical materials data, are held back by the difficulty of securing enough high-quality, high-precision training data. Computational science based on physical laws, meanwhile, faces surging computing costs as accuracy and scale increase. Quantum computers are seen as promising because they can simulate the behavior of molecules and electrons more efficiently than conventional computers, but current limits on qubit counts and error rates mean large-scale calculation on quantum machines alone is not yet a reality. The project therefore aims to set a methodology that blends quantum-based calculation with AI to soften those precision and scale constraints, rather than targeting one specific material.
- Hard to secure sufficient, high-quality, high-precision training data
- Surging computational costs for higher accuracy and larger-scale calculations
- Limited qubit counts and error rates keep large-scale calculations out of reach
- Verify whether quantum computing applies to major materials with high industrial demand, including materials for next-generation batteries, optical functional materials for new displays and sensors, and superconducting materials for next-generation devices.
- Develop technologies that integrate quantum-computing calculations with AI to widen the scope of computational methods and address the scale and cost limits of conventional approaches.
- Synthesize materials and evaluate their performance based on simulation results, including those using quantum computing, then compare experiment results against simulation data to improve prediction accuracy and verify the practical benefits of quantum computing.
- TOPPAN Holdings: run hybrid quantum-classical algorithms on quantum computers for additive materials, develop combined quantum computing and AI methods, and provide experiment feedback.
- AISIN: verify quantum computing for superconducting materials, compare results with conventional material development methods, and forecast practical implementation timelines in material development environments.
- Asahi Kasei: verify platforms for materials development by studying quantum computing's applicability to organic compounds such as electrolytes, polymer materials, and inorganic materials such as electrodes, combined with feedback from experiments.
- SoftBank Corp.: use a computing platform that links quantum computers with supercomputers to generate high-level data for materials development, develop AI-based data utilization technologies, and run demonstrations optimizing development across domains including energy-related materials such as next-generation batteries.
- AGC: develop and evaluate methods that combine quantum computing with existing computational science and AI for inorganic materials such as optical glass and organic materials such as electronic materials, and derive design guidelines to improve materials exploration and design.
- DIC: integrate quantum computing, simulation and data science to predict key material properties before synthesis for colorants and semiconductor materials, and establish and validate new research methodologies for efficient design of high-performance materials.
The work will not focus on a single material: the technologies are to be applied cross-sectionally across a broad range of industrially vital materials systems, with each of the six companies handling verification in its own area of strength. The companies also plan to work with specialized quantum computing institutions to verify applicability across diverse materials and to create practical value for industrial use. Looking ahead, they intend to combine quantum computing and AI data with the experimental data generated during the project to build a continuously evolving materials development platform that contributes to cross-industry innovation.
Why this matters
The six companies say Japanese chemical and materials manufacturers need faster, cheaper ways to find new materials to stay globally competitive. The project is meant to work around the two weak spots of today's methods — scarce high-quality training data for AI and rising computing costs for simulations — and it spans industrially important materials from battery and electrode materials to optical glass and superconducting materials, so each company verifies results in its own field of strength.
Terms in This Story
- qubit
- The fundamental unit of quantum information in quantum computing; today's machines have limited numbers of them and relatively high error rates.
- NEDO
- Japan's New Energy and Industrial Technology Development Organization, a public body that solicits and funds industrial technology research projects.
- computational science
- Simulating physical phenomena on computers using the laws of physics, an approach used to predict how materials will behave.
Summarised from the linked release; details can be imperfect — always verify against the original source.