Centre for Quantum Information, Simulation and Algorithms
Quantum computing has made significant strides since the introduction of Shor's factoring algorithm (1995) and Grover's search algorithm (1996). We now know that a quantum computer can efficiently solve vast sets of linear equations, simulate diverse Hamiltonians for physical and biological systems, perform various linear transformations such as Fourier transforms, and effectively evaluate inner products and distances in extremely high-dimensional vector spaces. Quantum computing offers the prospect of harnessing nature at a much deeper level than ever before, opening up a wealth of new possibilities for information processing and data analysis. QUISA is a multidisciplinary research team comprising experts from physics, mathematics, computer science, bioinformatics, mathematical finance, medical physics, engineering, as well as high-performance computing. Together, we offer a diverse set of projects for 2025, ranging from theoretical and mathematical research to computational and practical applications. A list of potential supervisors and current research students is included at the end of this document. Please feel free to arrange a meeting with any of us, if you have questions or need further information.
This project focuses on developing engaging quantum games and puzzles as its main activity. These interactive tools are crafted to demystify and illustrate complex quantum principles through hands-on, immersive experiences. By incorporating fundamental concepts such as superposition, interference, entanglement, and quantum measurement into enjoyable and challenging formats, the project aims to make quantum computing accessible and captivating for learners of all ages. The objective is to offer an innovative educational approach that enhances comprehension and sparks a deeper interest in quantum technologies. Through this approach, participants will gain practical insights into quantum mechanics while having fun, effectively bridging the gap between theoretical concepts and their real-world applications.
Deep neural networks are the driving force behind the recent breakthroughs in AI. They are already widely deployed in everyday applications, including safety-critical domains (e.g., autonomous vehicles, medical diagnostics, and financial systems). The ability to formally verify the behaviour of these networks would provide substantial benefits, including reliability, safety and interpretability. Although many different algorithms for neural network verification are available, they suffer significant scaling challenges. As a result users have to compromise - either by reducing the size of the network which reduces its performance, or by simplifying the specification which less accurately captures the desired behaviour. However, some of the most successful verification algorithms take the form of a highly structured branching search. This suggests that the problem is potentially well-suited to quantum computing, which is particularly effective at exploring exponentially many branches in parallel. This project would be to look at the initial stages of developing algorithms for neural network verification that are capable of running on quantum hardware.
Quantum computing has made significant strides since the introduction of Shor's factoring algorithm (1995) and Grover's search algorithm (1996). We now know that a quantum computer can efficiently solve vast sets of linear equations, simulate diverse Hamiltonians for physical and biological systems, perform various linear transformations such as Fourier transforms, and effectively evaluate inner products and distances in extremely high-dimensional vector spaces. Quantum computing offers the prospect of harnessing nature at a much deeper level than ever before, opening up a wealth of new possibilities for information processing and data analysis. QUISA is a multidisciplinary research team comprising experts from physics, mathematics, computer science, bioinformatics, mathematical finance, medical physics, engineering, as well as high-performance computing. Together, we offer a diverse set of projects for 2025, ranging from theoretical and mathematical research to computational and practical applications. A list of potential supervisors and current research students is included at the end of this document. Please feel free to arrange a meeting with any of us, if you have questions or need further information.
This project focuses on developing engaging quantum games and puzzles as its main activity. These interactive tools are crafted to demystify and illustrate complex quantum principles through hands-on, immersive experiences. By incorporating fundamental concepts such as superposition, interference, entanglement, and quantum measurement into enjoyable and challenging formats, the project aims to make quantum computing accessible and captivating for learners of all ages. The objective is to offer an innovative educational approach that enhances comprehension and sparks a deeper interest in quantum technologies. Through this approach, participants will gain practical insights into quantum mechanics while having fun, effectively bridging the gap between theoretical concepts and their real-world applications.
Deep neural networks are the driving force behind the recent breakthroughs in AI. They are already widely deployed in everyday applications, including safety-critical domains (e.g., autonomous vehicles, medical diagnostics, and financial systems). The ability to formally verify the behaviour of these networks would provide substantial benefits, including reliability, safety and interpretability. Although many different algorithms for neural network verification are available, they suffer significant scaling challenges. As a result users have to compromise - either by reducing the size of the network which reduces its performance, or by simplifying the specification which less accurately captures the desired behaviour. However, some of the most successful verification algorithms take the form of a highly structured branching search. This suggests that the problem is potentially well-suited to quantum computing, which is particularly effective at exploring exponentially many branches in parallel. This project would be to look at the initial stages of developing algorithms for neural network verification that are capable of running on quantum hardware.
