28 citations · 35 across the 12 of their papers we have counts for
12 papers
Strong-coupling expansion and two-point Padé approximation for lattice field theory
Yuanran Zhu, Efekan Kökcü, Chao Yang
Reliable approximations for correlation functions at intermediate and strong coupling remain hard to obtain for general quantum field theories. Perturbative expansions are often as…
Global approximations to correlation functions of strongly interacting quantum field theories
Yuanran Zhu, Yang Yu, Efekan Kökcü +2
We introduce a method for constructing global approximations to correlation functions of strongly interacting quantum field theories, starting from perturbative results. The key id…
RELift: Learned Coarse-to-Fine Propagators for Time-Dependent PDEs with Applications to Electron Dynamics
Hardeep Bassi, Yuanran Zhu, Erika Ye +5
We present RELift (Restrict, Evolve, Lift), a two-phase learning framework that couples coarse-grid numerical solvers with neural operators to super-resolve and forecast fine-grid…
Predicting open quantum dynamics with data-informed quantum-classical dynamics
Pinchen Xie, Ke Wang, Anupam Mitra +4
We introduce a data-informed quantum-classical dynamics (DIQCD) approach for predicting the evolution of an open quantum system. The equation of motion in DIQCD is a Lindblad equat…
Compact representation and long-time extrapolation of real-time data for quantum systems using the ESPRIT algorithm
Andre Erpenbeck, Yuanran Zhu, Yang Yu +6
Representing real-time data as a sum of complex exponentials provides a compact form that enables both denoising and extrapolation. As a fully data-driven method, the Estimation of…
A Practical Framework for Simulating Time-Resolved Spectroscopy Based on a Real-time Dyson Expansion
Cian Reeves, Michael Kurniawan, Yuanran Zhu +5
Time-resolved spectroscopy is a powerful tool for probing electron dynamics in molecules and solids, revealing transient phenomena on sub-femtosecond timescales. The interpretation…
Transformer-based operator learning framework for self-energy in strongly correlated systems
Yuanran Zhu, Peter Rosenberg, Zhen Huang +3
We introduce -Attention, a Transformer-based operator-learning framework to address a key computational challenge in correlated materials. Our approach utilizes an Encoder-Only…
Unified error bounds for perturbations of non-Markovian open quantum systems in Gaussian environments
Zhen Huang, Yuanran Zhu, Gunhee Park +1
We present perturbative error bounds for the non-Markovian dynamics of observables in open quantum systems interacting with Gaussian environments, governed by a general Liouville d…
Quasi-Lindblad pseudomode theory for open quantum systems
Gunhee Park, Zhen Huang, Yuanran Zhu +3
We introduce a new framework to study the dynamics of open quantum systems with linearly coupled Gaussian baths. Our approach replaces the continuous bath with an auxiliary discret…
Predicting nonequilibrium Green's function dynamics and photoemission spectra via nonlinear integral operator learning
Yuanran Zhu, Jia Yin, Cian C. Reeves +2
Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and…
Performance of wave function and Green's functions based methods for non equilibrium many-body dynamics
Cian C. Reeves, Gaurav Harsha, Avijit Shee +5
Theoretical descriptions of non equilibrium dynamics of quantum many-body systems essentially employ either (i) explicit treatments, relying on truncation of the expansion of the m…
Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations
Hardeep Bassi, Yuanran Zhu, Senwei Liang +4
In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-di…