Quantum machine learning for efficient spike sorting in low-cost neural recording systems

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. 

The School of Electronics and Computer Science 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.