Training Course Professional

Quantum Computing for Media Recommendation and Audience Personalisation

Evaluating quantum optimisation and QML for recommendation engines and personalisation at scale.

Full day (6 hours)

Certificate on completion

Enquire About This Course View Curriculum

Proud to recommend our expert members

Course Overview

Technical workshop examining quantum machine learning for content recommendation and audience personalisation. Covers quantum kernel methods for catalogue embedding, variational quantum circuits for collaborative filtering, dequantisation limits, and honest NISQ hardware assessment against production streaming workloads.

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 Recommendation at Scale Where quantum approaches enter the recommendation pipeline
2	Quantum Kernel Methods for Content Similarity Quantum kernel estimation and feature maps for catalogue embedding	Quantum kernel estimation (QKE): mapping content features to high-dimensional Hilbert space for similarity computation Quantum feature maps versus classical RBF and polynomial kernels: theoretical expressivity and the role of data structure Practical constraints: current QKE implementations on IBM Eagle (127 qubits) require circuit depths that exceed coherence times for production-scale catalogues
Break
3	Variational Quantum Circuits for Collaborative Filtering VQC architectures for user preference modelling	Variational quantum circuit (VQC) design for collaborative filtering: parameterised ansatz, entanglement structure, and the barren plateau problem Hybrid quantum-classical training loops: quantum circuit as feature extractor feeding classical recommendation heads Published results: Netflix-scale matrix factorisation remains beyond NISQ capacity; sub-problems (cold-start user embedding) show more tractable circuit requirements
4	Interactive Demonstration Facilitator-led quantum recommendation pipeline walkthrough	Live demonstration: quantum kernel similarity computation on a sample content catalogue using Qiskit Runtime on IBM Quantum Interpreting kernel matrices: comparing quantum versus classical similarity rankings on the same dataset Discussion: identifying which stages of your recommendation pipeline could benefit from quantum feature maps versus where classical methods remain superior
Break
5	Dequantisation and Classical Benchmarking Honest assessment of quantum advantage claims	Dequantisation results (Tang 2019, subsequent work): classical algorithms that match quantum speedups for low-rank recommendation problems When quantum advantage survives dequantisation: high-rank, non-sparse data structures where classical sampling fails Benchmarking framework: how to evaluate vendor quantum ML claims against classical baselines on your own data
6	Readiness Assessment and Timeline NISQ limitations and fault-tolerant horizon	Current NISQ hardware constraints: qubit counts, gate fidelities, and coherence times versus production recommendation workloads Fault-tolerant timeline: logical qubit requirements for catalogue-scale quantum ML (estimated 2030-2035 for commercially relevant problem sizes) Near-term value: quantum-inspired classical algorithms (tensor networks, random feature sampling) that deliver today
7	Q&A and Roadmap Discussion

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.

Enquire About This Course

Get in touch to discuss scheduling, group pricing, and how this course can be configured to your organisation's needs.

Training Courses Consulting Services Training

Quantum Computing for Media Recommendation and Audience Personalisation

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

Enquire About This Course