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Quantum Computing for Urban Planning, Simulation, and Digital Twins

Applying quantum simulation and optimisation to urban digital twin modelling and city-scale planning.

Full day (6 hours)

Certificate on completion

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Course Overview

For urban planning teams and city digital twin architects. Covers quantum-enhanced agent-based modelling for population mobility simulation, QAOA for land use zoning optimisation, and QML for digital twin predictive analytics. Includes honest NISQ assessments against MATSim, Gurobi, and classical ML 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	Urban Simulation as a Computational Problem Where classical simulation hits scaling limits in city planning
2	Quantum-Enhanced Agent-Based Modelling QML for population mobility and urban flow simulation	Agent-based models for urban systems: population movement, commuter flows, and pedestrian dynamics at city scale with 100,000+ agents Quantum machine learning for agent behaviour prediction: parameterised quantum circuits as surrogate models for agent decision functions NISQ reality check: current hardware handles roughly 20-50 qubit circuits, limiting agent model complexity. Classical ABM tools (MATSim, SUMO) remain the production standard.
Break
3	Quantum Optimisation for Land Use Planning QUBO formulations for zoning, density, and infrastructure placement	Land use allocation as a combinatorial optimisation problem: zoning constraints, density targets, and transport accessibility as QUBO penalty terms QAOA for infrastructure placement: school, hospital, and transit stop location optimisation under population growth scenarios Benchmark-specific performance comparisons: QAOA on 50-100 variable zoning instances versus Gurobi and simulated annealing on equivalent problems
4	Interactive Demonstration Quantum-enhanced digital twin predictive analytics	Facilitator-led demonstration of a parameterised quantum circuit predicting energy demand patterns from a city digital twin sensor feed Comparing VQC prediction accuracy against gradient boosting and LSTM baselines on a 12-month urban energy consumption dataset Interpreting results: where quantum circuit expressibility adds value for multi-modal city data and where classical ML is sufficient
Break
5	Digital Twin Integration and NISQ Constraints Connecting quantum models to existing city data platforms	CityGML, FIWARE NGSI-LD, and digital twin data pipelines: where quantum processing modules can be inserted into existing city platform architectures Qubit count requirements: urban planning problems that fit on near-term hardware (50-100 qubits) versus those requiring fault-tolerant machines (2028+ timeline) Hybrid classical-quantum workflows: using quantum subroutines for specific simulation bottlenecks while classical HPC handles the broader model
6	Q&A and Research Roadmap

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 Urban Planning, Simulation, and Digital Twins

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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