26th Australian Institute of Physics Congress and the Australian and New Zealand Conference on Optics and Photonics

Session

AIP QST: Quantum machine learning

QST

7 Dec 2026, 15:45

Belinda Hutchinson Building (The University of Sydney )

36. Photonic Quantum-Accelerated Machine Learning

Andrew White (University of Queensland)

07/12/2026, 15:45

AIP | Quantum Science and Technology (QST)

Invited talk

Machine learning and quantum physics are two of the most transformative fields of science and technology. Their interaction is a rapidyl evolving field of research. Here, we introduce Quantum Optical Reservoir Computing (QORC)—a photonic accelerator for classical ML—in which a boson‑sampling interferometer acts as a fixed, high‑dimensional nonlinear quantum reservoir [1], enabling...

287. A hardware efficient quantum residual neural network without post-selection

Akib Karim (CSIRO)

07/12/2026, 16:15

AIP | Quantum Science and Technology (QST)

Contributed Oral

We propose a hardware efficient quantum residual neural network that implements non-unitary operations using ancilla qubits to allow for the same expressivity but significantly lower depth than traditional unitary quantum machine learning. In contrast to previous implementations of residual connections, our architecture avoids post-selection to ensure minimal measurement overhead. In order to...

354. Designing Noise-Robust CNOTs for Variational Quantum Classifiers using Optimal Quantum Control Techniques

Dr Eromanga Adermann (CSIRO)

07/12/2026, 16:30

AIP | Quantum Science and Technology (QST)

Contributed Oral

Quantum machine learning (QML) has the potential to revolutionise artificial intelligence, but its realisation is limited by the fragility of Noisy Intermediate Scale Quantum (NISQ) devices. Although quantum error correction (QEC) is expected to enable fault-tolerant QML, its substantial qubit overhead motivates the search for alternative strategies suitable for near-term hardware.
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280. Continuous-Variable Quantum Learning with Squeezed States

Angela Anna Baiju (Australian National University)

07/12/2026, 16:45

AIP | Quantum Science and Technology (QST)

Contributed Oral

While learning theory provides a framework for inferring unknown models from data, quantum inference tasks, such as state discrimination and parameter estimation, face unique constraints imposed by statistical limits and the fundamental laws of quantum mechanics. A central challenge is determining how efficiently information can be extracted from limited quantum resources.

In this work, we...

426. Selective quantum error correction on non-Clifford operations in quantum machine learning

Dylan O'Donoghue (The University of Western Australia)

07/12/2026, 17:00

AIP | Quantum Science and Technology (QST)

Contributed Oral

Errors in current quantum computers significantly limit the practicality and scalability of quantum algorithms, as noise accumulates in deep circuits and degrades quantum information. Although quantum error correction (QEC) offers a path toward fault-tolerant computation, the substantial spacetime overhead of existing protocols limits their feasibility on near-term devices. Building on recent...