Hybrid Quantum–Classical Optimisation for Intelligent Urban Transport Systems

Project overview

This project aims to design and evaluate a hybrid optimisation framework using Qiskit and complementary classical solvers to address complex urban transport optimisation challenges.

The research will benchmark quantum-assisted and classical optimisation methods in terms of accuracy, scalability, and computational efficiency, and explore how hybrid algorithms can improve routing, scheduling, and energy management in next-generation urban mobility systems.

Potential outcomes include prototype implementations for quantum-enhanced transport planning tools, comparative performance studies, and publications in quantum algorithms, transport optimisation, and intelligent systems.

Aim/outline

• Formulate intelligent urban transport optimisation tasks (e.g., EV/AV Vehicle Routing Problem variants, scheduling, charging coordination etc) as combinatorial optimisation problems.
• Implement QAOA/VQE in Qiskit using parameterised quantum circuits and appropriate problem encodings for transport-related objectives and constraints.
• Integrate classical optimisation routines (e.g., COBYLA, SPSA, NELDER–MEAD) to drive hybrid quantum–classical parameter updates.
• Benchmark the hybrid approach against classical optimisation algorithms, comparing solution quality, runtime performance, robustness, and resource requirements.
• Analyse scalability with respect to problem size, topology, and transport constraints, and evaluate the implications for NISQ-era quantum devices and future intelligent mobility systems (EVs, AV fleets, micro-grid scheduling).

URLs/references

• https://en.wikipedia.org/wiki/Vehicle_routing_problem
• https://aamircheema.com/research/SIGSPATIAL2024_EVs_VisionPaper.pdf
• https://aamircheema.com/research/SIGSPATIAL2025_AVs_Vision.pdf
• https://quantum.cloud.ibm.com/learning/en

Required knowledge

• Intermediate Python programming.
• Understanding of optimisation or algorithm design.
• Basic quantum computing theory and circuit representation.
• Familiarity with scientific libraries (SciPy, NumPy).
• Familiarity with Qiskit or other quantum SDKs (preferred but not essential).
• Interest in transport systems, EV/AV technologies, or intelligent infrastructure.