All Outputs (7)

Topographic Control of Order in Quasi-2D Granular Phase Transitions (2021)
Journal Article
Downs, J. G., Smith, N. D., Mandadapu, K. K., Garrahan, J. P., & Smith, M. I. (2021). Topographic Control of Order in Quasi-2D Granular Phase Transitions. Physical Review Letters, 127(26), Article 268002. https://doi.org/10.1103/PhysRevLett.127.268002

We experimentally investigate the nature of 2D phase transitions in a quasi-2D granular fluid. Using a surface decorated with periodically spaced dimples we observe interfacial tension between coexisting granular liquid and crystal phases. Measuremen... Read More about Topographic Control of Order in Quasi-2D Granular Phase Transitions.

Reinforcement learning of rare diffusive dynamics (2021)
Journal Article
Das, A., Rose, D. C., Garrahan, J. P., & Limmer, D. T. (2021). Reinforcement learning of rare diffusive dynamics. Journal of Chemical Physics, 155(13), Article 134105. https://doi.org/10.1063/5.0057323

We present a method to probe rare molecular dynamics trajectories directly using reinforcement learning. We consider trajectories that are conditioned to transition between regions of configuration space in finite time, such as those relevant in the... Read More about Reinforcement learning of rare diffusive dynamics.

Solvable class of non-Markovian quantum multipartite dynamics (2021)
Journal Article
Budini, A. A., & Garrahan, J. P. (2021). Solvable class of non-Markovian quantum multipartite dynamics. Physical Review A, 104(3), Article 032206. https://doi.org/10.1103/PhysRevA.104.032206

We study a class of multipartite open quantum dynamics for systems with an arbitrary number of qubits. The non-Markovian quantum master equation can involve arbitrary single or multipartite and time-dependent dissipative coupling mechanisms, expresse... Read More about Solvable class of non-Markovian quantum multipartite dynamics.

Theory of classical metastability in open quantum systems (2021)
Journal Article
Macieszczak, K., Rose, D. C., Lesanovsky, I., & Garrahan, J. P. (2021). Theory of classical metastability in open quantum systems. Physical Review Research, 3(3), 1-26. https://doi.org/10.1103/PhysRevResearch.3.033047

We present a general theory of classical metastability in open quantum systems. Metastability is a consequence of a large separation in timescales in the dynamics, leading to the existence of a regime when states of the system appear stationary, befo... Read More about Theory of classical metastability in open quantum systems.

Large Deviations at Level 2.5 for Markovian Open Quantum Systems: Quantum Jumps and Quantum State Diffusion (2021)
Journal Article
Carollo, F., Garrahan, J. P., & Jack, R. L. (2021). Large Deviations at Level 2.5 for Markovian Open Quantum Systems: Quantum Jumps and Quantum State Diffusion. Journal of Statistical Physics, 184(1), Article 13. https://doi.org/10.1007/s10955-021-02799-x

We consider quantum stochastic processes and discuss a level 2.5 large deviation formalism providing an explicit and complete characterisation of fluctuations of time-averaged quantities, in the large-time limit. We analyse two classes of quantum sto... Read More about Large Deviations at Level 2.5 for Markovian Open Quantum Systems: Quantum Jumps and Quantum State Diffusion.

Optimal sampling of dynamical large deviations via matrix product states (2021)
Journal Article
Causer, L., Bañuls, M. C., & Garrahan, J. P. (2021). Optimal sampling of dynamical large deviations via matrix product states. Physical Review E, 103(6), Article 062144. https://doi.org/10.1103/PhysRevE.103.062144

The large deviation statistics of dynamical observables is encoded in the spectral properties of deformed Markov generators. Recent works have shown that tensor network methods are well suited to compute accurately the relevant leading eigenvalues an... Read More about Optimal sampling of dynamical large deviations via matrix product states.

A reinforcement learning approach to rare trajectory sampling (2021)
Journal Article
Rose, D. C., Mair, J. F., & Garrahan, J. P. (2021). A reinforcement learning approach to rare trajectory sampling. New Journal of Physics, 23, Article 013013. https://doi.org/10.1088/1367-2630/abd7bd

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... Read More about A reinforcement learning approach to rare trajectory sampling.