Consulting / Artificial Intelligence

Quantum Education Workshops for AI and Machine Learning Teams

Six specialist workshops covering PQC migration for AI model weights and training infrastructure, quantum threats to federated learning, quantum machine learning capabilities, and cryptographic security for AI inference APIs. Delivered by practitioners with direct AI and quantum technology experience.

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Model weight security

Federated learning

Quantum ML

AI API security

Executive quantum strategy

Workshop Topics

All sessions are configurable for your audience and delivered by practitioners with direct AI sector and quantum technology experience.

PQC Protection for AI Model Weights and Training Infrastructure

Half day In person or online

Covers the cryptographic exposure of large language model weights, training checkpoint files, and distributed training cluster communications. Addresses NIST PQC standards for securing model artefact storage, cryptographic signing of model versions, and post-quantum secure NCCL and MPI collective communications in GPU training clusters. Aimed at MLOps engineers, AI platform security teams, and AI governance leads.

Cryptographic exposure of model weights: storage encryption and transmission risks
Post-quantum signing of model versions and release artefacts
NCCL and MPI inter-node communications in GPU training clusters: quantum risk and PQC options
Key management for distributed training infrastructure at scale
NIST FIPS 203 and 204 integration into MLOps pipelines and model registries

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Quantum Threats to Federated Learning and Privacy-Preserving AI

Half day In person or online

Addresses the quantum cryptographic exposure in federated learning frameworks including TensorFlow Federated, PySyft, and FATE. Covers secure aggregation protocol vulnerabilities, differential privacy mechanisms under quantum attack, and PQC migration for homomorphic encryption schemes used in privacy-preserving machine learning. Aimed at AI researchers, data privacy engineers, and organisations deploying federated AI across sensitive data.

Secure aggregation protocol vulnerability in federated learning under quantum threat
PQC migration for homomorphic encryption in privacy-preserving ML workloads
Differential privacy mechanisms: quantum attack surface and mitigation approaches
Quantum risks in TensorFlow Federated and FATE deployment architectures
Post-quantum secure multi-party computation for collaborative AI training

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Quantum Machine Learning: Current Capabilities and Enterprise Use Cases

Half day In person or online

Technical workshop examining the state of quantum machine learning: variational quantum circuits, quantum kernel methods, quantum neural networks, and quantum-enhanced optimisation for ML training. Separates credible near-term use cases from speculative long-term claims, examines performance comparisons with classical ML on benchmark tasks, and identifies where quantum ML offers genuine advantage versus marketing noise.

Variational quantum circuits: architecture, training, and practical performance limits
Quantum kernel methods and their comparison to classical kernel SVM approaches
Quantum-enhanced gradient descent and optimisation for ML training convergence
Benchmark review: quantum ML versus classical deep learning on real datasets
Near-term use case identification: where quantum ML is worth evaluating today

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Cryptographic Security of AI APIs and Inference Infrastructure

Half day In person or online

Covers the quantum cryptographic exposure of AI inference APIs, model serving infrastructure, and LLM deployment pipelines. Addresses TLS security for AI API endpoints, authentication token cryptography, and the specific risks of long-lived API keys used in agentic AI systems. Covers PQC migration for AI-as-a-service architectures and the intersection with OWASP LLM Top 10 security considerations.

Cryptographic exposure of AI API endpoints and long-lived authentication tokens
PQC for TLS in AI model serving and inference infrastructure
Agentic AI system authentication: quantum risk in multi-step LLM workflows
OWASP LLM Top 10 and quantum security: where the threat models intersect
PQC migration for AI-as-a-service platforms and enterprise LLM deployments

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Quantum Computing for AI Training Acceleration and Optimisation

Half day In person or online

Examines how quantum computing may accelerate specific bottlenecks in AI model training: hyperparameter search, neural architecture search, combinatorial feature selection, and attention mechanism optimisation. Covers realistic timelines for quantum advantage in AI training tasks, current hardware constraints relative to GPU cluster scales, and how to structure a quantum-AI research agenda for an enterprise AI team.

Quantum approaches to hyperparameter and neural architecture search
Combinatorial feature selection using quantum optimisation algorithms
Attention mechanism optimisation: quantum approaches and current hardware limits
Realistic timelines for quantum advantage in AI training versus GPU scaling
Structuring a quantum-AI research agenda for enterprise teams

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Quantum AI Strategy for CISOs, CTOs, and AI Governance Leaders

4 hours + Q&A In person or online Max 30 delegates

Executive briefing for CISOs, CTOs, and AI governance leaders at enterprises with significant AI investment. Covers the dual agenda: quantum computing as an AI capability accelerator alongside quantum security as a critical protection requirement for AI assets. Addresses emerging EU AI Act intersections with quantum security obligations, board-level AI risk governance, and investment prioritisation for quantum-ready AI infrastructure.

Quantum security obligations for AI systems under emerging EU and US regulation
Protecting AI competitive advantage: quantum threats to proprietary model assets
Quantum computing as an AI capability accelerator: a sector-specific assessment
EU AI Act and quantum security: where the obligations intersect
Investment sequencing: AI security hardening versus quantum-AI research investment

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All workshop participants benefit from QSECDEF membership. Members receive a 20% discount on in-person sessions and full access to the lecture archive and certification programmes.

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Sessions are configured around your team's technical level and operational priorities. Get in touch to discuss requirements and schedule a date.