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pro vyhledávání: '"Mair, Jamie F."'
We show how the trajectories of $d$-dimensional cellular automata (CA) can be used to determine the ground states of $(d+1)$-dimensional classical spin models, and we characterise their quantum phase transition, when in the presence of a transverse m
Externí odkaz:
http://arxiv.org/abs/2309.08059
Most iterative neural network training methods use estimates of the loss function over small random subsets (or minibatches) of the data to update the parameters, which aid in decoupling the training time from the (often very large) size of the train
Externí odkaz:
http://arxiv.org/abs/2306.13442
Publikováno v:
Phys. Rev. B 109, 024307 (2024)
Continuous-time quantum Monte Carlo refers to a class of algorithms designed to sample the thermal distribution of a quantum Hamiltonian through exact expansions of the Boltzmann exponential in terms of stochastic trajectories which are periodic in i
Externí odkaz:
http://arxiv.org/abs/2305.08935
We study the triangular plaquette model (TPM, also known as the Newman-Moore model) in the presence of a transverse magnetic field on a lattice with periodic boundaries in both spatial dimensions. We consider specifically the approach to the ground s
Externí odkaz:
http://arxiv.org/abs/2301.02826
In machine learning, there is renewed interest in neural network ensembles (NNEs), whereby predictions are obtained as an aggregate from a diverse set of smaller models, rather than from a single larger model. Here, we show how to define and train a
Externí odkaz:
http://arxiv.org/abs/2209.11116
Publikováno v:
New J. Phys. (2020)
Very often when studying non-equilibrium systems one is interested in analysing dynamical behaviour that occurs with very low probability, so called rare events. In practice, since rare events are by definition atypical, they are often difficult to a
Externí odkaz:
http://arxiv.org/abs/2005.12890
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