Quantum Computing Inc.: GIC 2026

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.

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.

The power grids of countries around the world are changing and developing with increased demand and awareness of issues like sustainability and climate change mitigation. At the same time, the resilience of these grids is of vital importance as disruptions to electricity can cause widespread damage in a region.

In planning a nation’s power grid, an integrated model of distributed resources can make a power grid more robust by breaking it down into a network of subgrids, or microgrids. These microgrids combine storage and generators to supply energy to consumers, while being isolated from each other to maintain the load locally, representing a highly flexible and optimizable building block for modern energy infrastructure, providing an opportunity to optimize power delivery by automatically switching from connected to disconnected modes and serving critical infrastructure. While thermal power generation is typically modeled with a quadratic cost function, the addition of a cubic term to the cost function provides better fidelity to real-world operation. Entropy quantum computing represents a method of optimizing cubic cost functions that could provide faster optimization of a network of microgrids.

Your challenge is to use quantum computing methods to optimize the cost functions of a network of microgrids under multiple simulated scenarios that may be faced in real-world power grids.