Simulation-based Quantum Machine Learning for Advancing AI

We are seeking a highly motivated candidate to pursue a PhD opportunity in the exciting and rapidly growing field of simulation-based quantum machine learning to shape the future of AI and quantum computing. As a member of our research team, you will have the opportunity to explore the cutting-edge intersection of quantum computing and machine learning to develop novel algorithms that can handle complex data structures and solve problems intractable by classical computing.

Research Focus:

Your research will involve working on the development and implementation of simulation-based quantum artificial intelligence and machine learning algorithms and models. The innovations will be applied to address real-world challenges across various domains such as healthcare, finance, and energy. Your research journey will commence with the design and simulation of elementary machine learning circuits and progressively advance to more complex quantum deep learning network such as Quantum Convolutional Neural Networks (QCNN).

Leverage Leading Tools:

You will work with a range of python-based open-source quantum software platforms and toolboxs, including IBM Qiskit, Google Cirq, Quantum Virtual Machine, cross-platform Python library PennyLane, and QuTip.

Diverse Research Directions:

Within this project, there are several potential research directions to explore, including:

·               Developing and implementing quantum machine learning algorithms for financial applications, like fraud credit card transaction detection.

·               Enhancing medical diagnoses and treatment planning using QCNN to analysis large datasets of patient information and medical imaging for applications such as dementia diagnosis.

·               Applying quantum machine learning to optimise resource allocation, increase efficiency, and reduce carbon emissions in energy systems.

In addition to your research, you will have the opportunity to receive specialised course and tutorial training on Quantum Machine Learning, collaborate with researchers in the fields of quantum computing and machine learning and participate in conferences and workshops to present your research findings.

The Ideal Candidate:

The ideal candidate will have a strong background in computer science, physics, optical communications, mathematics, or a related field, with a keen interest in machine learning, artificial intelligence and quantum computing. While prior experience with quantum mechanics, quantum circuit design, linear algebra, programming languages like Python, Tensorflow, and simulation software such as Qiskit or QuTiP is desirable, it is not a requirement.

Join our team and help shape the future of AI and quantum computing.

Qualified applicants are encouraged to contact Dr. Xing Liang (X.Liang@kingston.ac.uk) to discuss their application.