2 citations · 4 across the 8 of their papers we have counts for
8 papers
Variational quantum and neural quantum states algorithms for the linear complementarity problem
Saibal De, Oliver Knitter, Rohan Kodati +3
Variational quantum algorithms (VQAs) are promising hybrid quantum-classical methods designed to leverage the computational advantages of quantum computing while mitigating the lim…
Space-Time Spectral Element Tensor Network Approach for Time Dependent Convection Diffusion Reaction Equation with Variable Coefficients
Dibyendu Adak, Duc P. Truong, Radoslav Vuchkov +5
In this paper, we present a new space-time Petrov-Galerkin-like method. This method utilizes a mixed formulation of Tensor Train (TT) and Quantized Tensor Train (QTT), designed for…
Accurate Data-Driven Surrogates of Dynamical Systems for Forward Propagation of Uncertainty
Saibal De, Reese E. Jones, Hemanth Kolla
Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for…
Efficient Computation of Tucker Decomposition for Streaming Scientific Data Compression
Saibal De, Zitong Li, Hemanth Kolla +1
The Tucker decomposition, an extension of singular value decomposition for higher-order tensors, is a useful tool in analysis and compression of large-scale scientific data. While…
Tensor-Train Compression of Discrete Element Method Simulation Data
Saibal De, Eduardo Corona, Paramsothy Jayakumar +1
We propose a framework for discrete scientific data compression based on the tensor-train (TT) decomposition. Our approach is tailored to handle unstructured output data from discr…
Continuous-variable neural-network quantum states and the quantum rotor model
James Stokes, Saibal De, Shravan Veerapaneni +1
We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization. A simple family of continuous-vari…
Overcoming barriers to scalability in variational quantum Monte Carlo
Tianchen Zhao, Saibal De, Brian Chen +2
The variational quantum Monte Carlo (VQMC) method received significant attention in the recent past because of its ability to overcome the curse of dimensionality inherent in many-…
Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries
Saibal De, Hadi Salehi, Alex Gorodetsky
Bayesian low-rank matrix factorization techniques have become an essential tool for relational data analysis and matrix completion. A standard approach is to assign zero-mean Gauss…