Overview
Background
I bring industry and academic experience in working on quantum error mitigation, quantum error correction, and quantum control theory to enable quantum computing demonstrations on near-term hardware. I am currently investigating the feasibility of combining error mitigation and error correction techniques with quantum machine learning algorithms at the University of Queensland. With Sally Shrapnel and partnering with the Queensland Digital Health Center (QDHeC), we are analysing the operational robustness of quantum machine learning, with an eye to digital health use-case discovery and testing. Prior to this, I worked on execution of dynamic circuits for error mitigation and quantum error correction applications at IBM Quantum (US) for three years. My work resulted in 3 patents and being recognised as one of IBM Research’s Top Technical Contributors in 2023 globally. I have also designed classical algorithms for noise filtering and prediction for trapped ions at the Quantum Control Laboratory in the University of Sydney, winning ARC EQUS inaugural Director’s Medal in Australia in 2019.
Availability
- Dr Riddhi Gupta is:
- Available for supervision
- Media expert
Fields of research
Qualifications
- Bachelor, University of Auckland New Zealand
- Bachelor (Honours) of Physics, University of Auckland New Zealand
- Doctoral (Research), The University of Sydney
- Journal Peer-Reviewer, American Physical Society, American Physical Society
Research interests
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Error correcting overhead for quantum algorithms
Contemporary quantum computing hardware is subject to challenging noise environments. For sufficiently well-behaved types of noise, it is anticipated that one is able to detect and correct errors in quantum computation in real-time using quantum error correction techniques. We explore the compatibility of error correction techniques with quantum algorithms such as in quantum chemistry applications. We turn to the question of how errors propagate in quantum algorithms, and what these insights suggest about efficient encoding schemes and decoding strategies for partial or fully error-corrected implementations of quantum algorithms, with a specific focus on quantum applications for the nascent fault-tolerant regime.
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Noise characterization and error mitigation for quantum algorithms
Contemporary quantum computing hardware is subject to challenging noise environments. Well established techniques exist for noise characterization, and using information about the learned noise environment to improve the performance of quantum computing algorithms. We explore the efficacy and scalability of these techniques in regimes which violate underlying assumptions, e.g. about the noise process, or the properties of the quantum algorithm itself. Our goal is to use these insights to help scale-up quantum algorithms to intermediate scales on hardware, with a specific focus on quantum applications for the nascent fault-tolerant regime.
Works
Search Professor Riddhi Gupta’s works on UQ eSpace
2025
Journal Article
A systematic review of quantum machine learning for digital health
Gupta, Riddhi S., Wood, Carolyn E., Engstrom, Teyl, Pole, Jason D. and Shrapnel, Sally (2025). A systematic review of quantum machine learning for digital health. npj Digital Medicine, 8 (1) 237, 237-1. doi: 10.1038/s41746-025-01597-z
2024
Journal Article
Probabilistic error cancellation for dynamic quantum circuits
Gupta, Riddhi S., van den Berg, Ewout, Takita, Maika, Riste, Diego, Temme, Kristan and Kandala, Abhinav (2024). Probabilistic error cancellation for dynamic quantum circuits. Physical Review A, 109 (6) 062617, 1-13. doi: 10.1103/physreva.109.062617
2024
Journal Article
Encoding a magic state with beyond break-even fidelity
Gupta, Riddhi S., Sundaresan, Neereja, Alexander, Thomas, Wood, Christopher J., Merkel, Seth T., Healy, Michael B., Hillenbrand, Marius, Jochym-O'Connor, Tomas, Wootton, James R., Yoder, Theodore J., Cross, Andrew W., Takita, Maika and Brown, Benjamin J. (2024). Encoding a magic state with beyond break-even fidelity. Nature, 625 (7994), 1-24. doi: 10.1038/s41586-023-06846-3
2021
Journal Article
Adaptive filtering of projective quantum measurements using discrete stochastic methods
Gupta, Riddhi Swaroop and Biercuk, Michael J. (2021). Adaptive filtering of projective quantum measurements using discrete stochastic methods. Physical Review A, 104 (1) 012412, 1-12. doi: 10.1103/physreva.104.012412
2020
Journal Article
Integration of spectator qubits into quantum computer architectures for hardware tune-up and calibration
Gupta, Riddhi Swaroop, Govia, Luke C. G. and Biercuk, Michael J. (2020). Integration of spectator qubits into quantum computer architectures for hardware tune-up and calibration. Physical Review A, 102 (4) 042611, 1-13. doi: 10.1103/physreva.102.042611
2020
Journal Article
Adaptive characterization of spatially inhomogeneous fields and errors in qubit registers
Gupta, Riddhi Swaroop, Edmunds, Claire L., Milne, Alistair R., Hempel, Cornelius and Biercuk, Michael J. (2020). Adaptive characterization of spatially inhomogeneous fields and errors in qubit registers. npj Quantum Information, 6 (1) 53, 1-10. doi: 10.1038/s41534-020-0286-0
2018
Journal Article
Machine Learning for Predictive Estimation of Qubit Dynamics Subject to Dephasing
Gupta, Riddhi Swaroop and Biercuk, Michael J. (2018). Machine Learning for Predictive Estimation of Qubit Dynamics Subject to Dephasing. Physical Review Applied, 9 (6) 064042, 1-32. doi: 10.1103/physrevapplied.9.064042
Funding
Current funding
Supervision
Availability
- Dr Riddhi Gupta is:
- Available for supervision
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Available projects
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Navigating nascent fault-tolerant regimes with imperfect quantum error correction
Quantum computing is concerned with processing information according to the rules of quantum mechanics in order to solve learning problems. A key challenge is protecting quantum information processing systems from coupling uncontrollably to its broader physical environment, leading to errors in quantum computation. Fault tolerance refers to a regime for quantum computing where errors in quantum computation do not grow uncontrollably with the size of the computation. For sufficiently well-behaved errors in this regime, we expect to be able to detect and even correct errors in quantum computation 'on the fly' using quantum error correction methods. However, quantum computers expected in the next decade are expected to offer modest levels of error correction, where the infrastructure for error correction itself is anticipated to be error-prone. There are multiple Honours and Ph.D. theory projects available in this topic area. We are interested in developing metrics to quantify and compare how different error correcting methods fail under different noise models (e.g. leakage, drift); developing a theoretical framework to use information from quantum error correcting codes (i.e. syndrome measurements) to develop new techniques to mitigate errors on quantum gates or measurements; and developing error-corrected protocols for non-Clifford quantum operations. Candidates should expect to complete Honours-level or third-year courses in quantum information theory with a strong background in mathematical theory.
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Error-mitigation tools for quantum chemistry
Near-term quantum computing chemistry applications assume that for sufficiently large physical systems, it is possible to use quantum computers as part of a broader information processing pipeline to solve problems in chemistry. In particular, we study the utility of quantum-selected configuration interaction (QSCI) methods to simulate the behavior of molecules consisting of 100s or 1000s of atoms, with a view to informing chemical behavior in drug design and biomedical applications. A number of application-specific projects are available, for example, to understand and mitigate the effect of noise in QSCI methods; to inform trial-state design in QSCI methods; to investigate the efficiency of quantum sampling as one scales to larger active spaces. Ideal candidates should expect to complete Honours-level or third-year courses in quantum information theory and a background in mathematical theory, chemistry, or computer science is advantageous.
Supervision history
Current supervision
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Doctor Philosophy
Characterising the impact of noise in quantum machine learning.
Associate Advisor
Other advisors: Associate Professor Sally Shrapnel
Media
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