Quantum Innovation Challenge: Joint-Loss Modelling for Disaster Risk Financing

Problem & Approach (25%)

How clearly the team defines the problem and places it against the current state of the art. We assess whether the approach is technically feasible on current or near-term quantum and AI hardware, and whether it offers a new perspective or a demonstrated or credible advantage over classical methods (speed-up, accuracy or resource efficiency). We also assess whether it can scale to larger datasets and be adapted to other use cases.

Resource Estimation (10%)

Whether the team has realistically estimated the computing resources the solution needs. This covers classical resources (GPUs, CPUs) and, where relevant, quantum resources (qubit count, circuit depth, number of shots, simulator vs. hardware access). We also check that the estimate fits the challenge timeline.

Technical Rigor (20%)

Whether the chosen algorithms suit the task and the reasons for the chosen architectures and techniques are sound. We also look at whether existing quantum and classical solutions are identified and benchmarked against the proposed method.

Work Plan (15%)

Whether there is a clear, step-by-step roadmap with milestones for tackling the challenge. We also assess whether the main risks and failure points are identified, with credible fallback options or alternative solutions.

Team Skills (10%)

Whether the team has the experience in quantum computing and AI/ML techniques needed to deliver the proposed solution, shown through relevant backgrounds, publications or past projects.

Proof of Concept (20%)

Whether the team has built a minimal working prototype and reports preliminary results. We assess how well the proof of concept tests the core ideas of the proposed approach.