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Quantum Computing for AI Training Acceleration and Optimisation

Evaluating quantum acceleration for gradient descent, hyperparameter search, and large model training pipelines.

Full day (6 hours)

Certificate on completion

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Course Overview

For enterprise AI teams and ML engineering leads. Examines quantum computing approaches to AI training bottlenecks: hyperparameter search, neural architecture search, feature selection, and attention mechanism research. Covers NISQ hardware limits, published benchmarks, and fault-tolerant timeline.

Curriculum

Course content is structured into sessions below. Each session is configurable to your organisation's environment, threat model, and objectives.

#	Session	Topics
1	Classical AI Training Bottlenecks and Where Quantum Applies Identifying the computational problems quantum can address
2	Quantum Optimisation for AI Training Tasks QAOA and variational methods for hyperparameter and architecture search	Hyperparameter search as combinatorial optimisation: QAOA formulation for learning rate, batch size, and regularisation tuning Neural architecture search (NAS): encoding architecture choices as QUBO problems for quantum annealing and gate-based solvers Combinatorial feature selection: quantum approaches to reducing feature spaces in high-dimensional datasets
Break
3	Quantum Machine Learning for Training Acceleration Quantum kernels, variational circuits, and attention mechanism research	Quantum kernel methods (QKE): encoding classical training data into quantum feature spaces for kernel-based learning Variational quantum circuits (VQC) as parametric models: barren plateau challenges and expressibility limits on NISQ hardware Attention mechanism research: early-stage quantum approaches to self-attention computation and their distance from practical deployment
4	Interactive Demonstration: Quantum-Classical Hybrid Training Pipeline Full-day format only	Facilitator demonstrates a QAOA-based hyperparameter optimisation pipeline using Qiskit and a cloud quantum backend Delegates observe and interpret results: comparing quantum solver output against classical Bayesian optimisation baseline Discussion: identifying which training bottlenecks in your organisation map to quantum-addressable problem structures
Break
5	Hardware Reality and Honest Performance Assessment What works on NISQ hardware and what requires fault tolerance	Current NISQ limits: qubit counts, gate fidelities, and circuit depths achievable for AI training acceleration tasks Published benchmarks: Google, IBM, and academic results on quantum-classical hybrid training pipelines Fault-tolerant timeline: when quantum hardware reaches scale for production AI training (2028-2032 estimates)
6	Vendor Landscape and Research Agenda Planning Independent guidance on structuring a quantum-AI programme	Quantum computing platforms for AI research: IBM, Google, IonQ, Quantinuum capability comparison for ML workloads Quantum-inspired classical solvers: when Fujitsu Digital Annealer or tensor network methods outperform actual quantum hardware Structuring a quantum-AI research programme: pilot design, resource allocation, and realistic milestone setting
7	Q&A and Pilot Planning

Designed and Delivered By

Training courses are designed and delivered by QSECDEF in collaboration with subject specialists. All instructors have direct experience in quantum technologies and security.

QSECDEF brings expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our membership spans 900+ organisations and 1,500+ professionals working at the intersection of quantum technologies and critical infrastructure security.

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Quantum Computing for AI Training Acceleration and Optimisation

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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