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20162021
most citedProvable superior accuracy in machine learned quantum models

5 citations · 5 across the 1 of their papers we have counts for

collaborators

8 papers

quant-ph20215 cited

Provable superior accuracy in machine learned quantum models

Chengran Yang, Andrew Garner, Feiyang Liu +5

In modelling complex processes, the potential past data that influence future expectations are immense. Models that track all this data are not only computationally wasteful but al…

quant-ph2019

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…

quant-ph2019

Semi-device-independent information processing with spatiotemporal degrees of freedom

Andrew J. P. Garner, Marius Krumm, Markus P. Mueller

Nonlocality, as demonstrated by the violation of Bell inequalities, enables device-independent cryptographic tasks that do not require users to trust their apparatus. In this artic…

quant-ph2019

Resource theories of multi-time processes: A window into quantum non-Markovianity

Graeme D. Berk, Andrew J. P. Garner, Benjamin Yadin +2

We investigate the conditions under which an uncontrollable background processes may be harnessed by an agent to perform a task that would otherwise be impossible within their oper…

quant-ph2018

On defining the Hamiltonian beyond quantum theory

Dominic Branford, Oscar C. O. Dahlsten, Andrew J. P. Garner

Energy is a crucial concept within classical and quantum physics. An essential tool to quantify energy is the Hamiltonian. Here, we consider how to define a Hamiltonian in general…

cond-mat.stat-mech2018

One-shot information-theoretical approaches to fluctuation theorems

Andrew J. P. Garner

Traditional thermodynamics governs the behaviour of large systems that evolve between states of thermal equilibrium. For these large systems, the mean values of thermodynamic quant…