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Quantum Machine Learning: Current Capabilities and Enterprise Use Cases

Practical assessment of QML readiness: variational circuits, kernel methods, and near-term enterprise applications.

Full day (6 hours)

Certificate on completion

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Course Overview

Technical workshop on quantum machine learning algorithms, honest capability assessment, and enterprise applications. Covers quantum kernel methods, variational quantum circuits, the barren plateau problem, and where QML shows genuine promise versus hype.

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 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
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
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

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 Machine Learning: Current Capabilities and Enterprise Use Cases

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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