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Quantum Computing for Quality Control and Defect Detection

Quantum image recognition and anomaly detection techniques for manufacturing quality assurance.

Full day (6 hours)

Certificate on completion

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Course Overview

For quality control leads, process engineers, and manufacturing R&D teams. Covers quantum machine learning approaches to automated visual inspection and sensor-based defect detection, including QKE, QSVM, and variational classifiers, with honest NISQ performance assessments against classical CNN baselines.

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 QC Baselines and Where They Plateau The computational limits of current defect detection
2	Quantum Machine Learning for Visual Inspection QKE, QSVM, and variational classifiers for defect detection	Quantum kernel estimation (QKE) for image feature spaces: encoding production line images into quantum feature maps Quantum support vector machines (QSVM) for binary defect/no-defect classification on structured inspection data Variational quantum classifiers (VQC) for multi-class defect categorisation: scratches, voids, dimensional deviations
Break
3	Quantum-Enhanced Sensor Fusion and Anomaly Detection Combining multi-sensor data streams with quantum processing	Quantum principal component analysis (qPCA) for dimensionality reduction across vibration, thermal, and acoustic sensor arrays Quantum reservoir computing for time-series anomaly detection on production line telemetry Hybrid classical-quantum pipelines: classical pre-processing, quantum feature extraction, classical post-processing
4	Interactive Demonstration: Quantum Defect Classification Pipeline Full-day format only	Facilitator-led walkthrough of a QSVM defect classifier built with PennyLane on simulated inspection data Interpreting quantum kernel matrices and classification boundaries versus classical SVM baseline Delegates discuss: which defect types and data characteristics suit quantum versus classical approaches in their production environment
Break
5	NISQ Hardware Constraints and Honest Performance Assessment What works today and what requires fault tolerance	Current NISQ limits for QML: qubit counts (50-100 noisy qubits), circuit depth limits, and barren plateau risks for image-scale problems Published benchmarks: quantum kernel classifiers versus ResNet/EfficientNet on structured defect datasets (IBM, Google, academic groups) Fault-tolerant timeline: what error-corrected hardware unlocks for production-scale visual inspection (2028-2032 estimates)
6	Deployment Architecture and Vendor Landscape Integrating quantum QC into production environments	Edge-cloud hybrid architectures: classical inference at line speed, quantum model retraining on cloud QPU Vendor assessment: IBM Qiskit Machine Learning, PennyLane, Amazon Braket, Quantinuum InQuanto for QML workloads Quantum-inspired classical alternatives: tensor network classifiers, Fujitsu Digital Annealer for optimisation-based QC
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 Computing for Quality Control and Defect Detection

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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