19 citations · 29 across the 2 of their papers we have counts for
7 papers
Quantum coarse-graining for extreme dimension reduction in modelling stochastic temporal dynamics
Thomas J. Elliott
Stochastic modelling of complex systems plays an essential, yet often computationally intensive role across the quantitative sciences. Recent advances in quantum information proces…
Memory compression and thermal efficiency of quantum implementations of non-deterministic hidden Markov models
Thomas J. Elliott
Stochastic modelling is an essential component of the quantitative sciences, with hidden Markov models (HMMs) often playing a central role. Concurrently, the rise of quantum techno…
Robust inference of memory structure for efficient quantum modelling of stochastic processes
Matthew Ho, Mile Gu, Thomas J. Elliott
A growing body of work has established the modelling of stochastic processes as a promising area of application for quantum techologies; it has been shown that quantum models are a…
Measures of distinguishability between stochastic processes
Chengran Yang, Felix C. Binder, Mile Gu +1
Quantifying how distinguishable two stochastic processes are lies at the heart of many fields, such as machine learning and quantitative finance. While several measures have been p…
Extreme dimensionality reduction with quantum modelling
Thomas J. Elliott, Chengran Yang, Felix C. Binder +3
Effective and efficient forecasting relies on identification of the relevant information contained in past observations -- the predictive features -- and isolating it from the rest…
Optimal stochastic modelling with unitary quantum dynamics
Qing Liu, Thomas. J. Elliott, Felix. C. Binder +2
Identifying and extracting the past information relevant to the future behaviour of stochastic processes is a central task in the quantitative sciences. Quantum models offer a prom…