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Quantum Computing for Autonomous Vehicle Optimisation and Route Planning

Evaluating QUBO and QAOA approaches for real-time route planning and multi-vehicle coordination.

Full day (6 hours)

Certificate on completion

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Course Overview

Covers QUBO formulations for vehicle routing problems, quantum algorithms for fleet optimisation, and EV charging network scheduling. Honest assessment of current hardware capabilities (15-40 vehicles with constraints) and the fault-tolerant timeline for fleet-scale problems.

Curriculum

Course content is structured into sessions below. Each session is configurable to your organisation's environment, threat model, and objectives.

#	Session	Topics
1	Combinatorial Optimisation Problems in Automotive Why fleet logistics is computationally hard	Vehicle routing problem (VRP) complexity and NP-hardness classification Fleet scheduling with real-world constraints: driver hours, time windows, capacity limits EV charging infrastructure planning as a facility location problem Traffic flow coordination and multi-agent route assignment
2	QUBO Formulations for Vehicle Routing and Fleet Management Encoding automotive logistics as quantum-native problems	Translating VRP constraints (time windows, capacity, multi-depot) into QUBO representations Penalty term design for route feasibility and constraint satisfaction Quantum annealing for TSP variants: encoding, embedding, and chain strength tuning Multi-depot fleet formulations and depot assignment as binary optimisation
Break
3	Quantum Algorithms for Logistics Optimisation QAOA, VQE, and annealing for automotive problems	QAOA (Quantum Approximate Optimisation Algorithm) applied to fleet scheduling VQE (Variational Quantum Eigensolver) for constrained vehicle routing Quantum annealing versus gate-based approaches: problem structure determines which fits Hybrid quantum-classical solvers and their role in near-term deployment
4	Hands-On: Solving a Fleet Routing Problem with Quantum Computing From problem formulation to hardware execution	Formulating a real fleet management scenario (multi-vehicle, time-windowed deliveries) as QUBO Running on D-Wave quantum annealer and IBM gate-based simulator Comparing quantum results against a classical OR-Tools baseline
Break
5	EV Charging Network Optimisation Where quantum approaches map naturally to the problem	Charging station placement as a combinatorial facility location problem Demand forecasting and grid load balancing with constrained optimisation Vehicle-to-grid (V2G) scheduling: time-slot allocation as binary optimisation Why EV infrastructure planning suits QUBO formulations particularly well
6	Current Capabilities and the Road Ahead Honest assessment of what works today and what does not	Fleet sizes solvable on current hardware: 15-40 vehicles with constraints on NISQ devices Quantum-inspired classical solvers (Fujitsu Digital Annealer, Toshiba SQBM+) for near-term deployment Fault-tolerant timeline and what it unlocks for fleet-scale combinatorial problems
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 Autonomous Vehicle Optimisation and Route Planning

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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