Build a Hybrid QKD/PQC Simulator with Secure Key Management using GPT-5 Pro and BoTorch

This challenge focuses on designing and simulating a hybrid secure communication network. Participants will implement a system that leverages Post-Quantum Cryptography (PQC) for initial secure channel establishment and classical data encryption, complemented by a simulated Quantum Key Distribution (QKD) layer for generating and distributing truly quantum-secure keys between multiple 'cities' or nodes. The system must demonstrate secure key negotiation, lifecycle management, and integration with a simulated application layer. The solution should incorporate advanced optimization techniques for QKD channel parameter selection and utilize modern AI tools for secure key management policy generation and network performance analysis. This challenge bridges the gap between theoretical quantum security and practical, scalable deployment in a classical network infrastructure.

• Master the theoretical foundations of the BB84 QKD protocol and its practical limitations (e.g., distance, error rates, photon loss).

• Implement a simulated QKD key generation and distribution mechanism in Python, including error correction (e.g., Cascade, Winnow) and privacy amplification.

• Integrate selected Post-Quantum Cryptography (PQC) algorithms (e.g., Kyber for KEM, Dilithium for signatures, using `pqc-py` or similar libraries) to establish initial secure communication channels between nodes.

• Design a secure key management system that handles key generation, distribution, rotation, and revocation for both PQC and QKD-derived keys across a simulated multi-city network using secure multi-party computation (MCP) principles for distributed trust.

• Optimize QKD transmission parameters (e.g., pulse intensity, detection efficiency) for maximum secure key rate and minimum Quantum Bit Error Rate (QBER) across a simulated noisy channel using `BoTorch` for Bayesian Optimization.

• Build a network simulator in Python (e.g., using `networkx` for topology, custom messaging layer) to model key exchange and secure data transmission between N nodes, incorporating both PQC and QKD layers.

• Leverage an LLM (e.g., simulated `GPT-5 Pro` API calls or local inference model for policy generation) to dynamically generate security policies for key usage, storage, and access control based on simulated threat intelligence.

• Develop a robust logging and monitoring framework to track QBER, key rates, and security events within the simulated network.