About the Project
Supervisory team: Professor Marian Florescu
Quantum physics and artificial intelligence are converging to change how complex light-matter systems are modelled and designed. This project will develop quantum reservoir computing as a theoretical and computational framework that uses the intrinsic dynamics of quantum systems to enable efficient learning, prediction and inverse design of photonic and quantum materials.
The research will advance quantum reservoir computing (QRC) as a computational paradigm for modelling and designing complex quantum and photonic systems. Rather than relying only on data-intensive or brute-force numerical approaches, QRC exploits coherence, memory and nonlinearity in quantum dynamics as resources for information processing and learning.
You will develop rigorous theoretical models and large-scale numerical simulations that reveal how quantum dynamics can perform tasks analogous to neural networks. The work will connect open quantum systems, photonics, machine learning and high-performance computing, with an emphasis on physically interpretable methods and architectures.
You will be based at the Optoelectronics Research Centre (ORC), a world-leading photonics institute with extensive experimental and computational facilities. The project offers training in quantum theory, numerical simulation, machine learning and high-performance computing, with access to large CPU and GPU clusters.
The research programme will focus on:
- establish physically interpretable QRC algorithms capable of capturing strongly correlated and open quantum systems
- explore quantum-neuromorphic architectures using computational models of superconducting, photonic and hybrid reservoirs, identifying routes to energy-efficient learning
- apply these frameworks to the inverse design of photonic and quantum structures, testing how QRC can accelerate discovery and optimisation in a fully computational setting
The School of Optoelectronics (ORC) is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees' well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
- physics
- photonics
- electrical or electronic engineering
- applied mathematics
- computer science
- a closely related discipline
Essential skills:
- experience of numerical modelling and scientific programming gained through an undergraduate, master's or research project
Desirable skills:
- prior study of quantum mechanics, open quantum systems, photonics or machine learning
- experience with Python, C++, Julia or MATLAB and/or use of high-performance computing environments
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
You need to:
- choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD ORC (7097)
- add name of the supervisor in section 2 of the application
Applications should include:
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
For questions about applying, doctoralcollege-admissions@soton.ac.uk.
Project leader
For an initial conversation, m.florescu@soton.ac.uk.