Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement the core quantum-inspired optimization algorithm (e.g., a VQE or QAOA on a classical simulator like Qiskit Aer, or a classical annealing algorithm with D-Wave's Ocean SDK) that takes problem parameters and returns optimized values. Then, develop a Prefect flow that integrates this solver. The flow should include tasks for problem definition, solver execution, and initial result capture. Ensure your flow is robust, with logging and error handling.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Optimize Industrial Design with Quantum-Inspired Algorithms & GenAI Workflow
This challenge involves leveraging quantum-inspired optimization techniques to solve a complex industrial design problem, such as materials selection or manufacturing process optimization for a jet engine component (inspired by Rolls-Royce's quantum simulation project). Participants will implement a quantum-inspired algorithm (e.g., QAOA, VQE on a classical simulator, or an annealing algorithm) to optimize key parameters. The innovation lies in orchestrating this optimization workflow using a multi-agent system powered by Qwen2.5-72B via CAMEL, managed by Prefect for robust scheduling and monitoring. The goal is to demonstrate how advanced classical optimization methods, informed by quantum principles, can be effectively integrated into an autonomous, AI-driven workflow for industrial applications, potentially running at the edge of a private 5G network. Success will be measured by the optimization algorithm's performance and the efficiency and robustness of the AI-orchestrated workflow.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.