Models only the costly post-VQE feedback; 1.6–2.0× fewer real VQE calls.
About
I am a PhD candidate at the School of Computing Technologies, RMIT University, working on AI for Quantum and Quantum for RecSys. My work aims to get reliable results from limited quantum hardware: fewer qubits, fewer shots and fewer VQE calls.
News
- Transferred from Master by Research to PhD at RMIT, with candidature counted from May 2025.
- FunnelAudit preprint on arXiv.
- PDQUBO published in ACM TORS.
- HamQASBench and DreamQAS preprints on arXiv.
- HyRLQAS preprint on arXiv.
- Q-EVT short paper at RecSys 2025.
Publications
My name is in bold. The full list is on Google Scholar.
AI for Quantum
Reinforcement learning for quantum architecture search.
HamQASBench: equally accurate circuits can use anywhere from 2 to 62 gates.
Learns gate placement and parameter initialization together; energy error down to 10⁻⁸.
Quantum for RecSys
Quantum optimisation for recommender systems on today’s quantum hardware.
Builds the QUBO from counterfactual effects of features and feature pairs on recommendation quality.
Recommendation with only five qubits, about 90% fewer than one qubit per tag.
First gate-based QPU study of recommender feature selection; predicts the shots needed to beat classical SA.
Other recommender systems work
Audits which retrieval routes are responsible for an item being shown or dropped.
Education
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2023 – present
RMIT University School of Computing Technologies, Melbourne
- Doctor of PhilosophyBegan as a fully funded Master by Research; transferred to PhD in Oct 2026, with candidature counted from May 2025.May 2025 – May 2029 (expected)
- Honours in Computer Science; not completed, transferred to MResJul 2024 – May 2025
- Remote Research InternOct 2023 – Jul 2024
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2019 – 2023
North China University of Science and Technology College of Science
- Bachelor of Science, Information and Computing ScienceSep 2019 – Jul 2023
Research assistant