19 citations · 29 across the 4 of their papers we have counts for
8 papers · 1 filter
Variational Quantum Dimension Reduction for Recurrent Quantum Models
Chufan Lyu, Ximing Wang, Mile Gu +2
Recurrent quantum models (RQMs) realize sequential quantum processes through repeated application of a unitary operation on a memory system coupled with a series of output register…
Energetic advantages for quantum agents in online execution of complex strategies
Jayne Thompson, Paul M. Riechers, Andrew J. P. Garner +2
Agents often execute complex strategies -- adapting their response to each input stimulus depending on past observations and actions. Here, we derive the minimal energetic cost for…
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…
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…