17 citations · 83 across the 13 of their papers we have counts for
13 papers
Bayesian perspectives for quantum states and application to ab initio quantum chemistry
Yannic Rath, Massimo Bortone, George H. Booth
The quantum many-electron problem is not just at the heart of condensed matter phenomena, but also essential for first-principles simulation of chemical phenomena. Strong correlati…
pyTTN: An Open Source Toolbox for Open and Closed System Quantum Dynamics Simulations Using Tree Tensor Networks
Lachlan P Lindoy, Daniel Rodrigo-Albert, Yannic Rath +1
We present the Python Tree Tensor Network package (pyTTN) for the evaluation of dynamical properties of closed and open quantum systems that makes use of Tree Tensor Network (TTN),…
Simple Fermionic backflow states via a systematically improvable tensor decomposition
Massimo Bortone, Yannic Rath, George H. Booth
We present an effective ansatz for the wave function of correlated electrons that brings closer the fields of machine learning parameterizations and tensor rank decompositions. We…
Fast characterization of multiplexed single-electron pumps with machine learning
N. Schoinas, Y. Rath, S. Norimoto +8
We present an efficient machine learning based automated framework for the fast tuning of single-electron pump devices into current quantization regimes. It uses a sparse measureme…
Fast and accurate nonadiabatic molecular dynamics enabled through variational interpolation of correlated electron wavefunctions
Kemal Atalar, Yannic Rath, Rachel Crespo-Otero +1
We build on the concept of eigenvector continuation to develop an efficient multi-state method for the rigorous and smooth interpolation of a small training set of many-body wavefu…
Interpolating many-body wave functions for accelerated molecular dynamics on the near-exact electronic surface
Yannic Rath, George H. Booth
While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a s…
Bayesian Modelling Approaches for Quantum States -- The Ultimate Gaussian Process States Handbook
Yannic Rath
Capturing the correlation emerging between constituents of many-body systems accurately is one of the key challenges for the appropriate description of various systems whose proper…
Impact of conditional modelling for a universal autoregressive quantum state
Massimo Bortone, Yannic Rath, George H. Booth
We present a generalized framework to adapt universal quantum state approximators, enabling them to satisfy rigorous normalization and autoregressive properties. We also introduce…
A framework for efficient ab initio electronic structure with Gaussian Process States
Yannic Rath, George H. Booth
We present a general framework for the efficient simulation of realistic fermionic systems with modern machine learning inspired representations of quantum many-body states, toward…
The quantum Gaussian process state: A kernel-inspired state with quantum support data
Yannic Rath, George H. Booth
We introduce the quantum Gaussian process state, motivated via a statistical inference for the wave function supported by a data set of unentangled product states. We show that thi…
A Bayesian Inference Framework for Compression and Prediction of Quantum States
Yannic Rath, Aldo Glielmo, George H. Booth
The recently introduced Gaussian Process State (GPS) provides a highly flexible, compact and physically insightful representation of quantum many-body states based on ideas from th…
Gaussian Process States: A data-driven representation of quantum many-body physics
Aldo Glielmo, Yannic Rath, Gabor Csanyi +2
We present a novel, non-parametric form for compactly representing entangled many-body quantum states, which we call a `Gaussian Process State'. In contrast to other approaches, we…
Prominent interference peaks in the dephasing Anderson model
Yannic Rath, Florian Mintert
The Anderson model with decoherence features a temporal evolution from localized eigenstates to a uniform spatial distribution bar any interference features. We discuss the growth…