35 citations · 171 across the 15 of their papers we have counts for
15 papers
Multicriticality in stochastic dynamics protected by self-duality
Konstantinos Sfairopoulos, Luke Causer, Juan P. Garrahan
We study the dynamical large deviations (LD) of a class of one-dimensional kinetically constrained models whose (tilted) generators can be mapped into themselves via duality transf…
Spin models from nonlinear cellular automata
Konstantinos Sfairopoulos, Luke Causer, Jamie F. Mair +2
We study classical and quantum spin models derived from one-dimensional cellular automata (CA) with nonlinear update rules, focusing on rules 30, 54 and 201. We argue that the clas…
Discrete generative diffusion models without stochastic differential equations: a tensor network approach
Luke Causer, Grant M. Rotskoff, Juan P. Garrahan
Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using…
Dynamical heterogeneity and large deviations in the open quantum East glass model from tensor networks
Luke Causer, Mari Carmen Bañuls, Juan P. Garrahan
We study the non-equilibrium dynamics of the dissipative quantum East model via numerical tensor networks. We use matrix product states to represent evolution under quantum-jump un…
Non-thermal eigenstates and slow relaxation in quantum Fredkin spin chains
Luke Causer, Mari Carmen Bañuls, Juan P. Garrahan
We study the dynamics and thermalization of the Fredkin spin chain, a system with local three-body interactions, particle conservation and explicit kinetic constraints. We consider…
Scalable simulation of non-equilibrium quantum dynamics via classically optimised unitary circuits
Luke Causer, Felix Jung, Asimpunya Mitra +2
The advent of near-term digital quantum computers could offer us an exciting opportunity to investigate quantum many-body phenomena beyond that of classical computing. To make the…
Cellular automata in dimensions and ground states of spin models in dimensions
Konstantinos Sfairopoulos, Luke Causer, Jamie F. Mair +1
We show how the trajectories of -dimensional cellular automata (CA) can be used to determine the ground states of -dimensional classical spin models, and we characterise…
Minibatch training of neural network ensembles via trajectory sampling
Jamie F. Mair, Luke Causer, Juan P. Garrahan
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 deco…
Rejection-free quantum Monte Carlo in continuous time from transition path sampling
Luke Causer, Konstantinos Sfairopoulos, Jamie F. Mair +1
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 e…
Boundary conditions dependence of the phase transition in the quantum Newman-Moore model
Konstantinos Sfairopoulos, Luke Causer, Jamie F. Mair +1
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 spa…
Optimal sampling of dynamical large deviations in two dimensions via tensor networks
Luke Causer, Mari Carmen Bañuls, Juan P. Garrahan
We use projected entangled-pair states (PEPS) to calculate the large deviations (LD) statistics of the dynamical activity of the two dimensional East model, and the two dimensional…
Slow dynamics and large deviations in classical stochastic Fredkin chains
Luke Causer, Juan P. Garrahan, Austen Lamacraft
The Fredkin spin chain serves as an interesting theoretical example of a quantum Hamiltonian whose ground state exhibits a phase transition between three distinct phases, one of wh…
Finite time large deviations via matrix product states
Luke Causer, Mari Carmen Bañuls, Juan P. Garrahan
Recent work has shown the effectiveness of tensor network methods for computing large deviation functions in constrained stochastic models in the infinite time limit. Here we show…
Optimal sampling of dynamical large deviations via matrix product states
Luke Causer, Mari Carmen Bañuls, Juan P. Garrahan
The large deviation (LD) statistics of dynamical observables is encoded in the spectral properties of deformed Markov generators. Recent works have shown that tensor network method…
Dynamics and large deviation transitions of the XOR-Fredrickson-Andersen kinetically constrained model
Luke Causer, Igor Lesanovsky, Mari Carmen Bañuls +1
We study a one-dimensional classical stochastic kinetically constrained model (KCM) inspired by Rydberg atoms in their "facilitated" regime, where sites can flip only if a single o…