U.S. Department of Energy OTC: GIC 2026

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.

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.

As electricity demand grows and the U.S. power system integrates data centers and large loads, grid planners must strategically determine where to deploy energy storage systems and microgrids to maximize resilience, reliability, and economic efficiency. Optimal siting decisions must account for load variability, generation variability, transmission constraints, contingency requirements, and varying weather risks, while balancing capital investment and operational performance over multi-year planning horizons.

Energy storage and microgrid placement problems are typically formulated as large-scale mixed-integer optimization models with nonlinear power flow constraints and multi-scenario uncertainty. These problems require evaluating thousands of potential infrastructure configurations across diverse operating conditions, making them computationally intensive and increasingly complex as fast-varying loads proliferate.

This challenge explores how near-term quantum computing approaches—particularly hybrid quantum-classical optimization and heuristic methods—can enhance strategic infrastructure planning for storage and microgrid deployment. Participants will investigate quantum formulations of siting and sizing decisions, develop mappings to QUBO or variational optimization frameworks, and explore hybrid decomposition techniques for handling multi-scenario resilience constraints.

Teams will benchmark quantum and hybrid approaches against established classical planning solvers using realistic grid test systems and resilience-focused case studies. The objective is not to replace classical tools, but to determine where quantum methods may improve combinatorial search efficiency, scenario exploration, solution robustness, or investment trade-off analysis.

Outcomes of this challenge have direct implications for accelerating new load integration, improving grid hardening strategies, enabling community-level resilience through microgrids, and strengthening U.S. energy security and infrastructure competitiveness.