Frances F Yang

Bridging quantum computing and computer vision — from quantum optimization to robust geometry and machine learning.

About Me

Frances Yang is a final-year PhD candidate at the Australian Institute for Machine Learning (AIML), The University of Adelaide, supervised by Prof. Tat-Jun Chin and Prof. Frank Neumann. Completing her PhD in 2026, she focuses on developing quantum algorithms for foundational problems in machine learning and computer vision, with a particular emphasis on quantum-enhanced geometric reasoning and optimization. She has experience working with both annealing-based and gate-based quantum platforms and has implemented scalable solutions for perception and decision-making tasks.

Frances is eager to join a team advancing quantum hardware, where she can contribute to showcasing hardware capabilities and improving quantum-classical workflows through rigorous algorithmic benchmarking and performance analysis. With a strong foundation in algorithm development, hands-on Python programming, and experience translating research into practical systems, she brings a deep commitment to pushing the boundaries of quantum computing in real-world applications.

Featured Publications

2024

ECCV
🌟Best Paper Candidate
🌟Oral

Robust fitting on a gate quantum computer

Frances Fengyi Yang, Michele Sasdelli, and Tat-Jun Chin

In European Conference on Computer Vision (ECCV), 2024

Bib PDF

@inproceedings{yang2024robust,
  title = {Robust fitting on a gate quantum computer},
  author = {<strong>Yang</strong>, <strong>Frances Fengyi</strong> and Sasdelli, Michele and Chin, Tat-Jun},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2024},
}

BMVC

Projected stochastic gradient descent with quantum annealed binary gradients

Maximilian Krahn, Michele Sasdelli, Frances Fengyi Yang, Vladislav Golyanik, Juho Kannala, Tat-Jun Chin, and Tolga Birdal

In British Machine Vision Conference (BMVC), 2024

Bib PDF

@inproceedings{krahn2023projected,
  title = {Projected stochastic gradient descent with quantum annealed binary gradients},
  author = {Krahn, Maximilian and Sasdelli, Michele and <strong>Yang</strong>, <strong>Frances Fengyi</strong> and Golyanik, Vladislav and Kannala, Juho and Chin, Tat-Jun and Birdal, Tolga},
  booktitle = {British Machine Vision Conference (BMVC)},
  year = {2024},
}

2023

arXiv

Training multilayer perceptrons by sampling with quantum annealers

Frances Fengyi Yang, Michele Sasdelli, and Tat-Jun Chin

In arXiv preprint, 2023

Bib PDF

@inproceedings{yang2023training,
  title = {Training multilayer perceptrons by sampling with quantum annealers},
  author = {<strong>Yang</strong>, <strong>Frances Fengyi</strong> and Sasdelli, Michele and Chin, Tat-Jun},
  booktitle = {arXiv preprint},
  year = {2023},
  featured = {true},
}

Research Directions

Quantum Machine Learning

I develop quantum-assisted learning methods that leverage quantum sampling for training. Representative projects include training multilayer perceptrons via quantum annealing-based sampling, and optimization with binary gradients on quantum annealers.

Quantum Computer Vision

I study how quantum computation can enhance core vision primitives, especially geometric estimation. A key project is demonstrating robust fitting on a real gate-model quantum computer, targeting practical quantum advantage for geometric reasoning pipelines.

Quantum Hardware Benchmarking

I benchmark quantum hardware and quantum-classical workflows with an algorithmic lens: scalability, robustness, and end-to-end performance. This includes systematic solver comparisons and device-aware evaluation across annealing and gate-based platforms.