About the project
The main challenge in the adoption of quantum computing is the gap between algorithmic requirements and current quantum hardware. In this project, you will get the chance to codevelop novel qubit efficient quantum algorithms that can be used to solve optimization problems and apply them to industries such as logistics, pharma, transport, finance or manufacturing.
The research will have both fundamental and applied science components. The former concerns the development, and benchmarking of the algorithms developed in simulators as well as cloud quantum computers; The latter will focus in translating the findings in quantum software applications Combinatorial binary optimization problems are known to be hard for classical computers.
Quantum solutions based on quantum digital, annealing and more recently variational algorithms, promise to solve such problems in certain cases faster and more efficiently. However, the requirements in the number of physical qubits needed to implement these algorithms, are still well beyond the reach of any near term quantum processor, which makes these approaches mostly academic in nature and challenging for industrial adoption. Recent efforts from the supervisor and collaborators have shown that is possible to develop novel qubit efficient variational algorithms, allowing for much larger problems to be tackled with near term quantum processors.
These were applied among others with success to a range of industrial use cases from optimizing of shipping routes, to financial optimization to aviation and energy management (see references). In this project, you will get the chance to be part of this exciting effort to translate theoretical quantum computing into tangible solutions for the world's most pressing challenges in the field of optimization.
The project will include funded visits to collaborating groups in Centre for Quantum Technologies Singapore and Greece, as well as leading theoretical and experimental teams in the US, Asia and the EU.
References
Qubit efficient algorithms for binary optimization problems” Quantum 5, 454 (2021). See research summary.
Qubit efficient quantum algorithms for the vehicle routing problem on quantum computers of the NISQ era, Adv. Quantum Technol. 20, 2300309 (2024). See research summary.
Exponential Qubit Reduction in Optimization for Financial Transaction Settlement Huber et al. See research summary.
The School of Electronics & 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.