Dr Riddhi Gupta on quantum machine learning and implications for health research

8 April 2024

Q&A: Meet Dr Riddhi Gupta - QDHeC Senior Research FellowDr Riddhi Gupta

Why did you decide to join UQ?

My relocation from the US to Australia felt a lot like coming home! Associate Professor Sally Shrapnel is one of the handful of first-movers leading use-case discovery in quantum machine learning for health and her group has a fantastic culture. UQ has a great history of research in quantum optics and quantum computing dating back to the 1990s and it was a great opportunity to continue to be a part of this research tradition. 

What is your current research focus?

While considerable progress has been made to build prototype quantum computers with hundreds of quantum bits, an open question is to find near-term use-cases that make quantum computing useful to society. We are exploring use-case discovery for quantum machine learning in health, including understanding how quantum computers perform under noise and the conditions under which meaningful quantum computing is attainable. 

What attracted you to quantum computing?

I loved quantum optics in my undergraduate physics degree but I detoured to do five years of management consulting before being attracted back to quantum physics. The pace of progress in quantum hardware was truly remarkable - accessing 5 qubits via a free, cloud based Python interface in the original IBM Q Experience in 2016 - seemed miraculous for a theorist. 

What is the hardest thing to communicate about quantum computing and quantum machine learning?

Dispelling the myth of quantum physics being hard or mysterious even when there are lots of pragmatic ways to participate in this area of research. There is a long-standing effort in preserving quantum mechanical properties of systems in the presence of realistic operating environments and understanding this impact of noise on algorithm development and use-case discovery is challenging. 

What excites you about your research?

Our research is fundamentally interdisciplinary. Within quantum physics, we are combining insights from different subfields such as quantum machine learning, tomography, error characterisation and mitigation, and algorithm design. Between health and physics, we have an opportunity to learn about classical machine learning and explore structure of real-world datasets by learning from our colleagues in QDHeC. 

How do you think your work - and the work of your colleagues - could positively impact health and science research?

Our industry and academic partners in Australia and around the world will be invaluable in shaping a view of what’s possible with quantum machine learning for health. We’re excited about informing this dialogue at a time where investment in quantum computing hardware is focused on overcoming challenges of scaling up quantum computers to increasingly larger size. Together these advancements allow algorithm development and experimental execution to be co-designed, making it an exciting time to explore use-case discovery for quantum computing. 

What do you enjoy doing outside of work?

My partner and I are loving our move to Queensland and we’re exploring a lot of places together for the first time.