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

Session

AIP QST: Quantum stochastic models & memory advantage

QST

8 Dec 2026, 14:30

Belinda Hutchinson Building (The University of Sydney )

778. Memory-efficient quantum transducers for input-output decoding

Jianjun Chen (Nanyang Technological University)

08/12/2026, 14:30

AIP | Quantum Science and Technology (QST)

Contributed Oral

Predicting a stochastic process requires memory, and the minimal memory that a model needs — its statistical complexity — can be reduced by using quantum rather than classical states. We focus on a concrete operational task: the decoding of convolutional codes over noisy channels. The theoretically optimal decoder tracks a continuous belief state, making its exact classical model...

549. Using Randomness to Bound Memory in Quantum Stochastic Processes

Derek Chang (Nanyang Technological University)

08/12/2026, 14:45

AIP | Quantum Science and Technology (QST)

Contributed Oral

To an observer with limited memory, even highly structured processes can appear random. This points to a fundamental interplay between randomness and temporal correlations present in a complex system's behavior. In characterizing this interplay, it is crucial to evaluate how much of a system's behavior is irreducibly random, and the minimum memory cost to simulate it perfectly. Classically,...

706. Optical Stochstic Simulator for Demonstration of Unbounded Quantum Memory Advantage

Brendan Wallis

08/12/2026, 15:00

AIP | Quantum Science and Technology (QST)

Contributed Oral

Stochastic processes are everywhere -- weather, financial markets, traffic -- and simulating them faithfully requires sufficient memory of past observations to predict future behaviour. For processes with a long historical dependence, the memory requirement grows with complexity posing a hard barrier for classical simulators. Quantum simulators offer an alternative through encoding memory in...

173. Stochastic Physical Neural Networks with Single Photons and Spin Chains

Mr Shiromal Kumara (School of Engineering and Technology, UNSW Canberra)

08/12/2026, 15:15

AIP | Quantum Science and Technology (QST)

Contributed Oral

The rapidly growing energy demands of AI have driven renewed interest in alternative approaches to computation. One alternative is physical neural networks (PNNs) in which inference and potentially training is performed directly by physical processes. Stochastic PNNs use stochasticity for computation, which is either classical (thermal) or quantum in nature. This stochasticity can dominate in...