Mitsubishi Chemical & AIST: GIC 2026

Advanced materials design drives innovation across the chemical and semiconductor industries, yet conventional computational approaches face limitations in accuracy and scalability when exploring complex molecular and material systems. This challenge centers on the Generative Quantum Eigensolver (GQE), an AI-driven quantum application that combines generative machine learning models with quantum eigensolvers to enable more accurate quantum simulations and efficient exploration of vast materials design spaces. Participants will investigate approaches for applying GQE within a Quantum Materials Informatics platform to improve quantum simulation accuracy for material properties, efficiently generate molecular and structural candidates, and accelerate materials discovery beyond the capabilities of classical simulation methods, including the simulation of extreme ultraviolet semiconductor materials.

Phases 2 and 3 of the Global Industry Challenge focus on developing a conceptual design (in Phase 2) to applied execution (in Phase 3), demonstrating technical sophistication and real-world relevance.

Phase 2 focuses on designing and justifying early-stage quantum solutions tailored to each challenge domain. Winning teams are expected to deliver, where possible, functional prototypes or circuit designs, define data preprocessing or encoding workflows, and justify their choice of algorithms and execution platforms, whether simulators or NISQ hardware. Equally important is demonstrating the value of the quantum approach compared to classical alternatives. In addition, teams should provide their estimates of the technical requirements needed to scale their solutions in Phase 3, including the type and extent of quantum hardware and simulator usage required via qBraid’s platform, IBM, D-Wave, or QCi. The selection of that device must be made on the Cover Page, or usage will not be granted.

Maximum 3 pages PDF (not including references or Cover Page Template), using 11-point Times New Roman font and single spacing, and submit it through the Aqora platform. Only submit one document per team.

Submission must follow GIC requirements: Maximum 3 pages (excluding this cover page and references), 11-point Times New Roman, single spacing, and submitted via Aqora. File Name Requirement: TeamName__Phase2_VersionX.pdf. This official cover page template is required and may not be modified or recreated.

Specific requirements are outlined in the Challenge Documents on the Challenge pages.
Generally, your submission should try to address:
·       Focus area and rationale
·       Technical approach to quantum integration
·       Stakeholder relevance
·       Data modelling strategy
·       Quantum platform justification and resource needs – details for accessing can be found below – selection details coming soon.

Important: Non-compliant submissions due to non-compliant page limit, use of the Cover Page template, or use of AI may be disqualified and voided.

Advanced materials design is a critical driver of innovation across the chemical and semiconductor industries, yet conventional computational approaches face fundamental limitations in accuracy and scalability when exploring complex molecular and material systems. Under Japan’s Strategic Innovation Promotion Program (SIP), Mitsubishi Chemical is leading an industry-driven effort to integrate artificial intelligence with quantum computing to overcome these barriers.

This challenge centers on the Generative Quantum Eigensolver (GQE)—an AI-driven quantum application jointly developed with AIST/G-QuAT, the University of Toronto, and NVIDIA. GQE combines generative machine learning models with quantum eigensolvers to enable more accurate quantum simulations and efficient exploration of vast materials design spaces. Participants will explore how GQE can be leveraged within a Quantum Materials Informatics (MI) Platform to accelerate materials discovery beyond the capabilities of classical simulation methods.

Your challenge is to investigate and propose approaches for applying GQE to materials informatics workflows, focusing on areas such as improving quantum simulation accuracy for material properties, efficiently generating molecular and structural candidates, and scaling exploration across complex chemical design spaces. As a concrete industrial use case, the challenge highlights the simulation of extreme ultraviolet (EUV) semiconductor materials, where high precision at the quantum level is essential.

Through this challenge, participants will gain hands-on exposure to cutting-edge quantum–AI integration and contribute ideas toward next-generation materials development with real-world industrial impact.