Quantum Machine Learning for Efficient Spike Sorting in Low-Cost Neural Recording Systems

About the Project

Supervisory Team: Dr Majid Zamani

This project explores quantum machine learning, specifically quantum kernel methods, to enable efficient spike sorting for resource-constrained neural recording systems. The goal is to overcome computational limitations of current methods, facilitating real-time applications like brain-computer interfaces and portable neurotechnology.

This project will investigate whether quantum machine learning (QML) approaches, particularly quantum kernel methods, can enable efficient spike sorting for resource-constrained neural recording systems. Extracellular neural recording systems capture electrical activity from populations of neurons, producing mixed signals that must be computationally separated, a process known as spike sorting. As neural recording technologies advance, enabling simultaneous recording from increasingly large neuronal populations, the computational demands of spike sorting have grown substantially.

Current high-performing approaches (such as Kilosort and MountainSort) often require significant computational resources, limiting their suitability for low-cost, embedded, or real-time applications like brain-computer interfaces (BCIs), neural prosthetics, and portable neurotechnology. Quantum machine learning, particularly hybrid quantum-classical models, offers a novel framework for classification in low-dimensional feature spaces. Quantum kernel methods may provide an efficient way to represent complex decision boundaries while maintaining a tractable processing pipeline suitable for deployment in low-cost systems.

Entry requirements

You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:

• physics
• computer science
• electrical engineering
• neuroscience
• a related field

Essential skills:

• strong background in at least two of the following: machine learning, quantum computing, signal processing, or neuroscience data analysis
• programming experience (Python preferred) and familiarity with scientific computing libraries
• excellent analytical and problem-solving skills

Desirable skills:

• familiarity with quantum computing frameworks (e.g., Qiskit, Pennylane, Cirq)
• knowledge of kernel methods or quantum machine learning

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

Apply now

You need to:

• choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Electronic & Electrical Engineering (7092)
• add name of the supervisor in section 2 of the application

Applications should include:

• research proposal
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (IELTS 6.5 overall, with at least 6.0 in each component).

Contact us

Faculty of Engineering and Physical Sciences

If you have a general question, feps-pgr-apply@soton.ac.uk.

Project leader

For an initial conversation, M.Zamani@soton.ac.uk.