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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop an `Optuna` study to optimize specific parameters of your PQC implementation (e.g., security levels affecting key size/latency, or specific library configurations). Define a clear objective function (e.g., minimizing handshake latency while maintaining a minimum security level) and an appropriate search space for Optuna. Run the optimization study and report the best parameters found and the observed performance improvements. Present your Optuna study code and results.
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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Build a PQC-Secured Communication Channel
The advent of quantum computers poses a significant threat to current cryptographic standards. This challenge focuses on building a prototype for a secure, post-quantum resilient communication channel. Participants will implement key exchange and digital signature mechanisms using a selected Post-Quantum Cryptography (PQC) algorithm (e.g., Kyber for KEM, Dilithium for signatures). The task involves integrating a chosen PQC library, demonstrating a secure handshake, and evaluating the performance overhead. To aid in algorithm selection and parameter tuning, participants are encouraged to leverage `Optuna` for hyperparameter optimization of PQC implementation parameters (e.g., security levels, speed/size trade-offs) and `DeepSeek-R1` for generating or analyzing efficient C/Python bindings for PQC primitives or even for understanding the underlying math. The solution should emphasize practical implementation, demonstrating the feasibility of PQC in real-world scenarios, and providing a foundation for future fault-tolerant quantum security applications.
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.