Workshops Artificial Intelligence Quantum Machine Learning Current Capabilities...

Artificial Intelligence Full Day Workshop

Quantum Machine Learning: Current Capabilities and Enterprise Use Cases

An honest assessment of quantum machine learning: what works, what does not, and where enterprise value exists today.

Full day (6 hours + Q&A)

In person or online

Max 30 delegates

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Workshop Description

Technical workshop covering quantum machine learning algorithms, their current capabilities on real hardware, and the enterprise applications where quantum approaches show genuine promise. Participants work through quantum kernel methods, variational quantum circuits, and a hands-on classifier exercise, then critically evaluate where QML adds value versus where classical ML remains the better choice.

Quantum machine learning is the most overhyped and most misunderstood area of quantum computing. Vendor marketing claims routinely conflate theoretical quantum advantage proofs with near-term practical capability. This workshop cuts through that. Quantum kernel methods, demonstrated experimentally by Havlicek et al. in Nature (2019), can outperform classical kernels on specific data distributions where the quantum feature space captures structure that classical feature maps miss. Variational quantum circuits provide a flexible framework for supervised learning but face the barren plateau problem: gradients vanish exponentially with circuit depth, making deep quantum neural networks untrainable with current methods (McClean et al., Nature Communications, 2018). Enterprise use cases exist but are narrower than marketing suggests. Drug discovery molecular property prediction with small training sets, financial anomaly detection, and materials property classification show credible results. Claims of general-purpose quantum ML superiority over classical deep learning are not supported by current evidence. The dequantisation programme (Tang, 2019 and subsequent work) has shown that some proposed quantum speedups can be matched classically. Participants leave understanding exactly where QML adds value and where classical ML remains the right choice.

What participants cover

Classical ML computational limits: the curse of dimensionality, kernel trick boundaries, and where deep learning plateaus create an opening for quantum approaches
Quantum kernel methods: quantum feature maps, quantum SVMs, and the conditions under which quantum kernels outperform classical alternatives
Variational quantum circuits: parameterised circuit architectures, data encoding strategies, and gradient computation via the parameter-shift rule
The barren plateau problem: why variational circuit trainability degrades exponentially with depth and what mitigation strategies exist
Enterprise use cases assessed honestly: drug discovery, financial anomaly detection, materials science, and the dequantisation threat to claimed quantum speedups
Hands-on classifier exercise: training a variational quantum classifier and benchmarking against a classical scikit-learn baseline

Preliminary Agenda

Full Day Workshop structure with scheduled breaks. Content is configurable to your organisation's ML maturity, data types, and priority use cases.

#	Session	Topics
1	Classical ML Limitations and Where Quantum Fits The curse of dimensionality, kernel trick boundaries, and the theoretical case for quantum advantage
2	Quantum Kernel Methods Quantum feature maps, quantum SVMs, and experimental evidence	Quantum feature maps: encoding classical data into quantum states for classification in exponentially large Hilbert spaces Quantum support vector machines and the quantum kernel estimation framework Havlicek et al. (Nature, 2019): experimental demonstration of quantum kernel advantage on specific data distributions When quantum kernels outperform classical kernels and when they do not
Break, after 50 min
3	Variational Quantum Circuits for Machine Learning Parameterised quantum circuits, data encoding, and training mechanics	Parameterised quantum circuits as trainable ML models: structure and expressibility Data encoding strategies: amplitude encoding, angle encoding, and basis encoding tradeoffs Training via the parameter-shift rule: analytical gradient computation on quantum hardware
4	Hands-On: Training a Variational Quantum Classifier Building and benchmarking a quantum ML pipeline	Encoding a real dataset into a parameterised quantum circuit using PennyLane or Qiskit Machine Learning Implementing the training loop: cost function, parameter-shift gradients, optimiser selection Measuring classification accuracy against a scikit-learn baseline on the same dataset
Break, after 75 min
5	The Barren Plateau Problem and Scalability Why deep variational circuits become untrainable	McClean et al. (Nature Communications, 2018): gradients vanish exponentially with circuit depth in random parameterised circuits The expressibility versus trainability tradeoff: more expressive circuits are harder to train Mitigation strategies: structured ansatze, layer-wise training, identity initialisation, and recent research directions
6	Enterprise Use Cases: Separating Signal from Noise Where QML shows credible results and where claims are unsupported	Drug discovery: molecular property prediction with small training sets as a credible near-term application Financial anomaly detection, materials science classification, and logistics optimisation: current evidence base The dequantisation programme (Tang, 2019 and subsequent work): classical algorithms matching proposed quantum speedups
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 and machine learning research.

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.

AI workshops are co-delivered with data science and machine learning specialists who bring direct experience in enterprise ML deployment, model evaluation, and research-to-production pipelines. This ensures workshop content is grounded in the practical realities of ML engineering, not just quantum theory.

Commission This Workshop

Sessions are configured around your ML team's current capabilities, data types, priority use cases, and quantum computing evaluation criteria. Get in touch to discuss requirements and schedule a date.

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