BigData Quantum Analytics

Course Syllabus

Module 1 — Big Data Engineering & Quantum Computing Foundations

• Introduction to Big Data ecosystems and distributed computing
• Foundations of quantum computing and quantum information science
• Apache Spark architecture and large-scale data processing
• Hadoop Distributed File System (HDFS) and data storage strategies
• Classical vs quantum computational models
• Qubits, superposition, entanglement, and quantum gates
• Quantum circuit fundamentals and quantum hardware overview
• Building scalable data pipelines for quantum-enhanced analytics

Module 2 — QUBO Models & Large-Scale Optimisation

• Introduction to combinatorial optimisation problems
• Quadratic Unconstrained Binary Optimisation (QUBO) formulation
• Simulated annealing and stochastic optimisation techniques
• Optimisation algorithms for large-scale enterprise systems
• Constraint modelling and objective function engineering
• Quantum-inspired optimisation methods
• Applications in logistics, scheduling, and portfolio optimisation
• Performance benchmarking and scalability analysis

Module 3 — Hybrid Quantum Machine Learning

• Introduction to Quantum Machine Learning (QML)
• Hybrid quantum-classical computing architectures
• Quantum neural networks and variational circuits
• Quantum kernels and feature mapping techniques
• Building hybrid models using PennyLane
• Integration of classical ML frameworks with quantum systems
• Quantum-enhanced classification and regression models
• Evaluating performance and computational advantages of QML

Module 4 — Quantum Annealing & Industrial Optimisation

• Quantum annealing principles and architectures
• D-Wave systems and quantum optimisation workflows
• Supply chain optimisation using quantum annealing
• Vehicle routing and resource allocation problems
• Manufacturing and operational efficiency optimisation
• Energy optimisation and scheduling applications
• Hybrid solvers and real-world deployment strategies
• Case studies in industrial quantum optimisation

Module 5 — Tensor Networks & Quantum Data Representation

• Introduction to tensor algebra and tensor networks
• Matrix Product States (MPS) and compressed representations
• High-dimensional data analysis using tensor methods
• Dimensionality reduction techniques with tensor networks
• Quantum many-body systems and data encoding
• Efficient representation of large-scale datasets
• Applications in machine learning and scientific computing
• Performance optimisation and scalability considerations

Module 6 — Production Quantum Pipelines & Capstone Project

• Designing production-ready quantum-inspired pipelines
• Cloud-based quantum computing platforms and services
• Introduction to AWS Braket and managed quantum infrastructure
• Deployment of hybrid quantum-classical applications
• Scalable orchestration and workflow automation
• Monitoring, optimisation, and performance evaluation
• Security and governance considerations for quantum systems
• Capstone project: Deploy a quantum-inspired optimisation or machine learning model using AWS Braket

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Starting Date: 4th July 2026

Course Fee: £3990

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