This project applies inverse design—an approach that starts from a desired performance and uses optimisation algorithms to create the system—to accelerate the development of advanced quantum devices. By integrating quantum mechanical models with cutting-edge optimisation techniques (e.g., adjoint methods, Bayesian optimisation, generative models), the research aims to autonomously design materials and structures that exhibit tailored quantum behaviours such as long coherence times, robust entanglement, and strong collective effects.

Key aspects:

• Focus: Computational frameworks for automated quantum device design.
• Methods: Gradient-based optimisation, machine learning, and open quantum systems modelling.
• Platforms: Potential applications include superconducting qubits, spin defects, and nanophotonic structures.
• Outcomes: Open-source tools, interpretable algorithms, and design blueprints for high-performance quantum technologies.
• Candidate profile: Strong background in physics, materials science, or engineering; interest in computational methods and quantum mechanics.