Quantum information processing combines concepts from quantum physics and information theory to demonstrate computational capabilities beyond those achievable with classical systems. Quantum computing is currently one of the fastest-growing areas within quantum science and technology, supported by substantial investment from governments, industry and startups worldwide. In particular, quantum optimisation has attracted considerable attention due to its potential applications in areas such as mining, transport, supply chain logistics, finance, pharmaceuticals and defence.
Although current quantum hardware remains affected by noise and hardware imperfections, rapid advances in quantum processor design are leading towards larger and more reliable quantum computing platforms. Variational quantum algorithms (VQAs) are among the most promising approaches for exploiting these emerging devices, as they combine quantum circuits with classical optimisation and can be adapted to realistic hardware constraints. This project investigates the development of robust and resource-efficient variational quantum optimisation algorithms suitable for utility-scale quantum computers. The research will include quantum circuit optimisation, noise-aware algorithm design, benchmarking protocols and analysis of computational resources required for practical implementations.
The findings of this research can contribute to the development of scalable quantum optimisation methods suitable for scientifically and industrially relevant applications.
The selected Ph.D. candidate will have the opportunity to engage in collaborative research activities with domestic and international partners, present research outcomes at conferences, and participate in seminars, group meetings and interdisciplinary discussions. The project will be co-supervised by experts in quantum computing and optimisation.<\/p>\n\n\n\n
Aim <\/p>\n\n\n\n
Quantum information is inherently sensitive to environmental noise and hardware imperfections, making robustness and scalability central challenges for practical quantum computation. The aim of this project is to develop variational quantum optimisation algorithms that remain effective under realistic hardware constraints while maintaining favourable computational resource requirements. The project will explore novel VQA architectures, optimisation strategies and error-resilient techniques suitable for next-generation quantum hardware.<\/p>\n\n\n\n
Objectives <\/p>\n\n\n\n
The project will investigate the implementation costs and performance characteristics of variational quantum optimisation protocols on realistic quantum computing platforms. This includes estimating resource requirements in terms of single- and two-qubit gate operations, analysing algorithmic robustness and benchmarking performance against classical optimisation approaches. The expected outcomes include publications in leading peer-reviewed journals, including those published by the American Physical Society.<\/p>\n\n\n\n
Significance <\/p>\n\n\n\n
Quantum information science has undergone several decades of intensive research and is now recognised as one of the key emerging technological areas worldwide. Within this ecosystem, quantum optimisation has become a major research direction because of its potential impact across a broad range of industries.
Variational quantum algorithms are widely regarded as one of the most practical approaches for exploiting near-term and utility-scale quantum hardware. However, significant challenges remain in understanding their scalability, robustness and potential computational advantages over classical methods. This project aims to address these challenges by developing and benchmarking practical VQA schemes tailored to realistic quantum architectures. The outcomes of this research may contribute to the development of scalable quantum optimisation methods for future quantum computing applications.<\/p>\n\n\n\n
Ideal Candidate <\/p>\n\n\n\n
We are looking for a self-motivated PhD candidate with excellent organisation, problem-solving and project management skills. It is expected that the candidate participates in seminars, group meetings, and other activities of scientific exchange. Candidates with strong quantitative skills, including familiarity with the following items are desired for this project:
1) background in theoretical physics, in particular, quantum information theory
2) keen interest in developing and applying quantum algorithm for optimisation problems
3) programming skills with Python and managing projects in cloud-based systems such as Github
4) Must be eligible to enrol in PhD programs at Curin<\/p>\n\n\n\n
This project is open to International and Domestic applicants. <\/p>\n\n\n\n
Internship<\/p>\n\n\n\n
Curtin Centre for Optimisation and Decision Science has already accomplished a quantum optimisation research project in cooperation with an industry partner. We are in the process of continuing that line of effort to foster fruitful collaboration towards our goals in tackling industry-scale applications.
An internship may be available for this project. The PhD candidate will work with high-performing researchers in the centre. The centre has extensive ongoing industry collaborations with major companies and government organisations. Some of the entities that the team has worked with include Alcoa, Roy Hill, Woodside Energy, Global Grain Handling Solutions, and Water Corporation. With the existing connections in the centre, the student will have various internship opportunities, to apply their research in industry projects.<\/p>\n\n\n\n
Scholarship <\/p>\n\n\n\n
If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship<\/a>. <\/p>\n\n\n\n Enquires and How to Apply <\/p>\n\n\n\n For enquires about this opportunity contact Dr Hoe Bui at Hoa.Bui@curtin.edu.au<\/a><\/p>\n\n\n\n