global-industry-challenge-2026 / 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.

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.

Submit your Phase 3 submissions via the Aqora Competition page for each of your chosen Challenge(s) due 11:59 PM (EST) on Sunday, July 26. Important: One submission is required per team and must match submission criteria or risk disqualification.