From building quantum networks to exploring the link between quantum computing and machine learning, the research of our four new postdoctoral fellows is set to have an impact.
We asked our new fellows to tell us about what they’re working on and what excites them about the future of quantum science.
Quantum and the stars
Dr Zixin Huang, Faculty of Science, Department of Physics and Astronomy, Macquarie University. Primary supervisor: Prof Gavin Brennen.
Research project: Large baseline quantum networks for super-resolution imaging.
My research focuses on quantum parameter estimation and imaging. Specifically, I exploit features of a quantum state or an optimised measurement. This approach provides greater sensitivity and resolution surpassing what is possible with classic technologies.
I see this work leading to the design of a large-baseline quantum imaging and sensing network. It is an interdisciplinary proposal with applications for astronomy. It could allow us to increase imaging resolution, by a factor of the ratio between the wavelength of microwaves to the optical. An improvement of three to five orders of magnitude.
In the words of Sir Arthur C. Clarke, "When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong."
What excites me about quantum technologies is that it opens endless possibilities - imagination is the only limit. With enhanced quantum imaging, we will be able to probe deeper into space, with unprecedented resolution compared to using classical optics alone.
I look forward to collaborating with SQA members, who have complementary expertise. This opportunity allows me to incorporate quantum error correction and network theory into my research.
Dr Maya Isarov, School of Electrical Engineering and Telecommunications, UNSW Sydney. Her supervisor will be Dr Arne Laucht.
Research project: A quantum interface based on split-vacancy colour centres in diamond integrated into a fibre cavity.
A quantum network is becoming essential to interconnect quantum computers and to enable significantly faster and more sophisticated computation. It also has the potential to expand into the quantum internet.
This novel quantum technology relies primarily on quantum nodes, which store data in their memory as spin states. The technology should entangle nodes coherently with photons transmitting data over distance through optical fibres. Each quantum node operates as a local spin-photon interface between quantum emitters and the optical fibre. My research aims to explore the ideal split-vacancy colour centre in diamond as an ultimate quantum emitter and form a functioning spin-photon interface by coupling it to a fibre cavity.
Integrated quantum emitters with cryogenic fibre cavities are integral to innovations in quantum technologies and the quantum industry, particularly in quantum networking. This is a unique approach to the realisation of a functioning spin-photon interface, which can be further scaled up to build efficient quantum nodes for quantum networking and lead to collaboration with industry.
Being part of the SQA and the Sydney quantum community is an ideal opportunity to work with leading researchers in the field on a timely and impactful project, as well as gain more professional experience. It ensures exposure of my project to the broader Australian quantum community and potentially educational and commercial opportunities.
Where’s that noise coming from?
Dr Robin Harper, Faculty of Science, School of Physics, the University of Sydney. Primary supervisor: Prof Stephen Bartlett.
Research project: SQUIB (Scalable QUantum Information Blueprints).
Quantum computers are inherently noisy. To fully realise their potential we will need to design them in a way that allows them to tolerate noise. This transition from current devices to fault-tolerant ones presents enormous challenges that will require the ability to efficiently identify and address errors that arise on large quantum systems. My research looks at how we can use the essential quantum nature of devices to extract the information we need about the noise in the device - to then identify, characterise, and correct it.
I’m looking to improve the metrology of smaller devices to give a detailed understanding of their noise processes – hopefully allowing better control of such devices. I’m also exploring how to extract relevant metrics from larger devices to aid in the execution of algorithms on current noisy devices and, hopefully, help pave the way to full error correction. The tools produced from my research are aimed at providing immediate and practical assistance to the scientists, engineers and companies who are working to create a quantum computer.
We live in an extremely exciting time as the devices being created begin to fulfil the theoretical promise of quantum computing. This year, we may well see the first reports of devices implementing full quantum error-correcting protocols. The need to be able to characterize the noise in devices much greater than 50 qubits will require fundamental breakthroughs in my field, which is an exciting challenge. For me, the ability to test my ideas and protocols on devices through the cloud is an amazing resource that continually fills me with wonder and enthusiasm.
The SQA is a fabulous mix of theorists and experimentalists, embodying the ecosystem and infrastructure required to make it a crucible of ideas. I look forward to discussing my research and ideas with the many members of the SQA family and being part of Sydney’s growth as a global hub for quantum technology.
Quantum algorithms and machine learning
Dr Maria Kieferova, Faculty of Engineering and Information Technology, School of Computer Science, University of Technology Sydney (UTS). Primary supervisor: Prof Michael Bremner.
Research project: Quantum advantage with quantum machine learning.
Many companies are wondering if quantum computers can solve some of their problems faster. My research helps to create quantum algorithms that can be adapted to solve specific industry problems.
I try to figure out how we should use a quantum computer once we build one. Firstly, I look for problems that appear easier for quantum computers than traditional ones, such as problems about quantum systems and problems that involve a lot of linear algebra. Then, I create quantum algorithms and calculate how much time and qubits they would need.
I will be looking into the intersection of quantum computing and machine learning. I believe that quantum computing can help to speed up some machine learning tasks. However, quantum machine learning algorithms are difficult to analyse, which presents an interesting challenge.
I have been in Sydney for four years, starting as a graduate student at Macquarie University and then as a postdoctoral researcher at UTS. Sydney has a strong quantum computing community, and I am looking forward to people from different universities and industry working more closely together.