activity
20142022
most citedTransfer learning for scalability of neural-network quantum states

55 citations · 160 across the 7 of their papers we have counts for

collaborators

8 papers

quant-ph2022★ 17 cited

Half-integer vs. integer effects in quantum synchronization of spin systems

Ryan Tan, Christoph Bruder, Martin Koppenhöfer

We study the quantum synchronization of a single spin driven by an external semiclassical signal for spin numbers larger than , the smallest system to host a quantum self-su…

cond-mat.str-el2020★ 15 cited

Melting of the critical behavior of a Tomonaga-Luttinger liquid under dephasing

Jean-Sebastien Bernier, Ryan Tan, Chu Guo +2

Strongly correlated quantum systems often display universal behavior as, in certain regimes, their properties are found to be independent of the microscopic details of the underlyi…

physics.comp-ph2020★ 4 cited

Finding Quantum Critical Points with Neural-Network Quantum States

Remmy Zen, Long My, Ryan Tan +5

Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body system…

cond-mat.stat-mech2020

Giant spin current rectification due to the interplay of negative differential conductance and a non-uniform magnetic field

Kang Hao Lee, Vinitha Balachandran, Ryan Tan +2

In XXZ chains, spin transport can be significantly suppressed when the interactions in the chain and the bias of the dissipative driving are large enough. This phenomenon of negati…

quant-ph2019★ 2 cited

Interaction-impeded relaxation in the presence of finite temperature baths

Ryan Tan, Xiansong Xu, Dario Poletti

We study the interplay between interactions and finite-temperature dephasing baths. We consider a double well with strongly interacting bosons coupled, via the density, to a bosoni…

physics.comp-ph2019★ 55 cited

Transfer learning for scalability of neural-network quantum states

Remmy Zen, Long My, Ryan Tan +5

Neural-network quantum states have shown great potential for the study of many-body quantum systems. In statistical machine learning, transfer learning designates protocols reusing…