Inverse design reverses the traditional design paradigm: instead of predicting the behaviour of a given system, it automatically creates a system that yields a desired target response. The goal of this PhD is to apply this principle to the design of next-generation quantum technologies. By combining cutting-edge optimisation algorithms with uniquely quantum effects, the project aims to accelerate the discovery and development of high-performance quantum devices with tailored functionality. 

Quantum technologies rely on precise control of quantum states in materials and engineered structures. Achieving long coherence times, robust entanglement, or tuneable quantum interactions often requires fine-tuning many physical parameters simultaneously. Traditional design approaches, which rely on intuition and trial-and-error exploration, are increasingly limited in navigating such complex, high-dimensional parameter spaces. Inverse design (see, e.g. [1,2]) provides a powerful alternative: by specifying a target quantum performance metric, efficient algorithms based on (for example) the adjoint method can “grow” an optimal structure or material configuration that realises it. 

This PhD will develop and apply computational frameworks that integrate quantum mechanical models with state-of-the-art optimisation techniques, building on previous high-profile work by the supervisory team [3]. Possible approaches include gradient-based inverse design using adjoint methods, Bayesian optimisation, and generative models capable of proposing entirely new classes of quantum materials or device architectures. The research will explore how these methods can be combined with realistic quantum descriptions of the real world (by, e.g., an open quantum systems approach) to ensure that designed systems are physically feasible and experimentally relevant. 

A key objective will be to autonomously propose materials and geometries that exhibit specific quantum behaviours, such as enhanced coherence, robust entanglement, or strong collective effects in photonic or solid-state systems. The project may focus on one or more quantum platforms, for example superconducting qubits, spin defects, or nanophotonic structures, depending on the candidate’s interests and available collaborations. There will be opportunities to interface with experimental groups to validate the predictions and to refine the inverse design algorithms based on experimental feedback. 

The candidate will develop expertise in quantum theory, computational physics and quantum devices. The project will involve designing, implementing, and testing inverse design pipelines that combine physical accuracy with computational efficiency. By embedding physical constraints [4] and quantum mechanical insight into optimisation algorithms [5], the research will aim to create interpretable and generalisable design tools. 

Expected outcomes include the development of new inverse design methods tailored to quantum technologies, open-source computational tools for quantum device optimisation, and design blueprints for materials or structures with boosted quantum performance. The research will contribute to the broader goal of making quantum device engineering more automated, efficient, and predictive. 

Applicants should have a strong background in physics, materials science, or engineering, and an interest in computational methods or machine learning. Familiarity with quantum mechanics and numerical simulation are a must. The project offers a unique opportunity to work at the intersection of quantum physics, artificial intelligence, and materials design, contributing to the foundations of future quantum technologies.