Advanced Materials

Mitsubishi Chemical & The National Institute of Advanced Industrial Science and Technology (AIST) 

Harnessing the Generative Quantum Eigensolver for Next-Generation Materials Design 

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.

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Energy Infrastructure

Quantum Computing Inc. 

Cost Optimization in Resilient Power Grids 

Power grids around the world are evolving with increased demand, sustainability goals, and the need for resilience against disruptions. Integrated models of distributed resources break the grid into networks of microgrids that combine storage and generators, operate in connected or disconnected modes, and serve critical infrastructure under real-world disruption scenarios. Optimizing these systems requires modeling thermal generation with higher-fidelity cubic cost functions and evaluating performance across multiple simulated scenarios. Participants will use quantum computing methods, including entropy quantum computing for optimizing cubic cost functions, to improve cost efficiency and resilience in microgrid networks while benchmarking performance against classical approaches.

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Energy Infrastructure

US Federal Agency (Confidential)

Quantum-Enhanced Strategic Siting of Energy Storage and Microgrids

As electricity demand grows and the U.S. power system integrates data centers and large industrial loads, grid planners must strategically determine where to deploy energy storage systems and microgrids to maximize resilience, reliability, and economic efficiency over multi-year planning horizons. These siting and sizing decisions must account for load variability, generation variability, transmission constraints, contingency requirements, and varying weather risks, requiring evaluation of thousands of potential infrastructure configurations across diverse operating conditions. Participants will investigate quantum formulations of siting decisions, develop mappings to QUBO or variational optimization frameworks, and benchmark hybrid quantum approaches against established classical planning solvers. The objective is to determine where quantum methods may improve combinatorial search efficiency, scenario exploration, solution robustness, or investment trade-off analysis for critical energy infrastructure.

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Dynamic Systems Forecasting

qBraid, MITRE, and Jones Trading

Quantum Reservoir Computing for Time-Series Intelligence

Quantum Reservoir Computing (QRC) is a near-term quantum machine learning paradigm for temporal data processing that requires no gradient-based optimization of the quantum system and is suited to noisy intermediate-scale quantum hardware. Participants will design and benchmark simulation-friendly QRC systems and apply them to real-world industry problems where chaotic dynamics and nonlinear temporal dependencies are central, including financial volatility prediction and climate and weather time-series forecasting. Teams will demonstrate performance across different qubit counts and realistic noise models, benchmark against classical baselines, and implement a common benchmark to validate that the quantum reservoir exhibits sufficient expressivity for forecasting complex, regime-shifting systems.