Workshops Defence Pattern Discovery in Intelligence Data

Defence Full Day Workshop

Quantum Pattern Discovery in Complex Intelligence Data

Intelligence analysis requires finding patterns in vast, noisy, multi-modal datasets: signals intelligence, geospatial imagery, human intelligence reports, and open-source feeds. Quantum machine learning algorithms claim to offer advantages for pattern recognition, classification, and anomaly detection. This workshop provides a technically rigorous assessment of what quantum ML can deliver today on NISQ hardware and where genuine advantage might emerge with fault-tolerant machines.

Full day (6 hours)

In person or online

Max 30 delegates

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Workshop Description

Quantum machine learning encompasses several algorithmic families with different maturity levels and different claims. Quantum kernel estimation (QKE) uses quantum circuits to compute similarity measures in high-dimensional feature spaces, potentially identifying patterns invisible to classical kernels. Quantum support vector machines (QSVM) apply these kernels to classification tasks. Quantum graph algorithms promise speedups for network analysis relevant to intelligence link analysis. However, dequantisation results (Tang 2019, subsequent work) have shown that many claimed quantum speedups evaporate when classical algorithms are given access to the same data structures.

This workshop gives intelligence data science teams an honest picture. Participants examine QKE, QSVM, and quantum graph algorithm implementations, understand the specific data characteristics where quantum kernels might outperform classical alternatives, and learn to identify overclaimed results in vendor presentations. The interactive demonstration compares quantum and classical kernel methods on a pattern recognition task representative of intelligence data analysis, showing both the potential and the current limitations.

What participants cover

Quantum kernel estimation (QKE): circuit design, feature maps, and kernel matrix computation
QSVM for classification: quantum advantage conditions and dequantisation limitations
Quantum graph algorithms: link analysis, community detection, and network flow for intelligence graphs
Barren plateaus and trainability: why variational quantum circuits struggle with large feature spaces
Dequantisation: when classical algorithms match quantum speedup claims (Tang 2019 and subsequent results)
Benchmark-specific performance comparisons on intelligence-representative datasets

Preliminary Agenda

Full day workshop structure with scheduled breaks. Content is configurable to your organisation's technical level and operational environment.

#	Session	Topics
1	Quantum Machine Learning for Intelligence Algorithms, claims, and reality	QKE and QSVM: how quantum circuits compute kernel functions for pattern recognition Quantum graph algorithms: Grover-enhanced search, quantum walk, and spectral analysis for link analysis The dequantisation challenge: when classical algorithms eliminate quantum speedups
2	Intelligence Data Characteristics Which data properties favour quantum approaches	High-dimensional sparse data: SIGINT feature extraction and quantum kernel applicability Graph-structured intelligence data: social networks, communication graphs, and financial flows Multi-modal fusion: combining SIGINT, GEOINT, HUMINT, and OSINT features for quantum classification
Break, after 60 min
3	Performance Analysis and Limitations Honest assessment of current capabilities	Barren plateaus: the fundamental training challenge for variational quantum circuits on large problems NISQ noise: how hardware errors degrade quantum kernel quality on current devices Classical baselines: gradient-boosted trees, deep learning, and classical kernel methods that quantum must beat
4	Interactive Demonstration Quantum versus classical pattern recognition	Facilitator-led comparison of quantum and classical kernel methods on a representative classification task Examining feature map design and its impact on quantum kernel discriminative power Interpreting results: accuracy, runtime, and scalability analysis
Break, after 90 min
5	Implementation Strategy for Intelligence Organisations Near-term and long-term planning	Quantum-inspired classical methods available today: random feature maps and tensor network classifiers Pilot programme design: selecting intelligence tasks suitable for quantum ML experimentation Data infrastructure requirements: quantum-classical hybrid processing pipelines
6	Case Studies: Quantum ML in Government Published results from intelligence and defence organisations	US intelligence community quantum ML pilots (publicly disclosed programmes) UK DSTL and European defence agency quantum computing initiatives
7	Q&A and Programme 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 technologies and defence 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.

Defence workshops are co-delivered with sector specialists who bring direct operational experience in defence organisations. This ensures workshop content is grounded in regulatory, operational, and technical realities specific to the sector.

Commission This Workshop

Sessions are configured around your organisation's technical level, operational environment, and regulatory jurisdiction. Get in touch to discuss requirements and schedule a date.

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.

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