Workshops Artificial Intelligence Quantum for AI Training

Artificial Intelligence Full Day or Half Day Workshop

Quantum Computing for AI Training Acceleration and Optimisation

Hyperparameter search, neural architecture search, and feature selection are combinatorial problems. Quantum optimisation algorithms offer a different approach to solving them. This workshop separates what works on current hardware from what requires fault tolerance.

Full day (6 hours) or half day

In person or online

Max 30 delegates

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Workshop Description

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.

Several AI training tasks are fundamentally combinatorial. Hyperparameter tuning over a large search space, neural architecture search across topology choices, and feature selection from high-dimensional datasets all map naturally to optimisation problems that quantum algorithms such as QAOA and quantum annealing address directly. Published results from Google AI Quantum, IBM Research, and academic groups show benchmark-specific performance comparisons between quantum-classical hybrid approaches and purely classical methods. The results are problem-dependent: for some search space structures, quantum approaches find competitive solutions faster; for others, classical Bayesian optimisation or random search still wins. This workshop maps which of your specific training bottlenecks have quantum-addressable structure, evaluates published evidence honestly, and assesses when fault-tolerant quantum hardware will reach the scale needed for production AI training workloads.

What participants cover

QAOA for hyperparameter optimisation: encoding learning rate, batch size, and regularisation search as QUBO problems
Neural architecture search: quantum annealing and gate-based approaches to topology selection as combinatorial optimisation
Quantum kernel methods (QKE) and variational circuits (VQC): where they show benchmark-specific advantages and where they do not
NISQ hardware assessment: current qubit counts, gate fidelities, and circuit depths relative to AI training acceleration requirements
Fault-tolerant timeline: when quantum hardware reaches production scale for enterprise AI workloads (2028-2032 estimates from IBM, Google, Quantinuum roadmaps)
Quantum-inspired classical alternatives: when tensor network methods, digital annealers, or simulated bifurcation outperform actual quantum hardware

Quantum technologies are evolving quickly and new developments emerge regularly. This page was last updated on 15/03/2026. For the most current information about course content and suitability for your organisation, we recommend contacting us directly.

Preliminary Agenda

Full-day session structure with scheduled breaks. Content is configurable to your AI training infrastructure, model types, and research priorities.

#	Session	Topics
1	Classical AI Training Bottlenecks and Where Quantum AppliesIdentifying the computational problems quantum can address
2	Quantum Optimisation for AI Training TasksQAOA 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, after 50 min
3	Quantum Machine Learning for Training AccelerationQuantum 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 PipelineFull-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, after 60 min
5	Hardware Reality and Honest Performance AssessmentWhat 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 PlanningIndependent 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

Workshops are designed and delivered by QSECDEF in collaboration with sector specialists. All facilitators have direct experience in both quantum computing algorithms and AI training systems.

QSECDEF brings world-leading expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our advisory membership spans 600+ organisations and 1,200+ professionals working at the intersection of quantum technologies and critical infrastructure security.

Quantum computing for AI workshops are co-delivered with specialists who bring direct experience in quantum algorithm research, ML engineering, and quantum-classical hybrid system design.

Commission This Workshop

Sessions are configured around your AI training infrastructure, model types, compute budget, and research priorities. Get in touch to discuss requirements and schedule a date.

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