48 citations · 237 across the 48 of their papers we have counts for
50 papers
Scalable quantum simulation of continuous-time generative models via tensor networks
Nathan X. Kodama, L. Andrew Wray, Sam Cochran +3
Continuous-time flow and diffusion models are widely used across many application domains, from large-scale deployment in computer vision and protein folding to emerging adoption f…
Equivariant Continuous Normalizing Flows with Offline Sampling for Fermionic Ground State Estimation
Sam Cochran, James B. Larsen, James Stokes +4
We introduce a framework for fermionic variational Monte Carlo in which a continuous normalizing flow refines a fixed antisymmetric base wavefunction. The flow is a permutation-equ…
A scalable Ewald-free BIE framework for periodic Stokes flow via hierarchical proxy sums
Tianyue Li, Dhairya Malhotra, Shravan Veerapaneni
Particulate Stokes flow in confined, periodic geometries underlies a broad class of problems in biophysics, microfluidics, and the rheology of complex fluids. Boundary integral equ…
Quantum Algorithms for Nonlinear Differential Equations via Pivot-Shifted Carleman Linearization
Ke Wang, Zikang Jia, Shravan Veerapaneni +1
We develop a pivot-shifted Carleman linearization framework for quantum algorithms solving quadratic nonlinear ordinary differential equations. By shifting the dynamics by a pivot…
Slip optimization on arbitrary 3D microswimmers: a reduced-dimension and boundary-integral framework
Marc Bonnet, Kausik Das, Shravan Veerapaneni +1
This article presents a computational framework for determining the optimal slip velocity of a microswimmer with arbitrary three-dimensional geometry suspended in a viscous fluid.…
Squirmers with arbitrary shape and slip: modeling, simulation, and optimization
Kausik Das, Hai Zhu, Marc Bonnet +1
We consider arbitrary-shaped microswimmers of spherical topology and propose a framework for expressing their slip velocity in terms of tangential basis functions defined on the bo…
Space-time adaptive methods for parabolic evolution equations
Jun Wang, Jie Su, Leslie Greengard +2
We present a family of integral equation-based solvers for the heat equation, reaction-diffusion systems, the unsteady Stokes equation and the incompressible Navier-Stokes equation…
Large Language Model Scaling Laws for Neural Quantum States in Quantum Chemistry
Oliver Knitter, Dan Zhao, Stefan Leichenauer +1
Scaling laws have been used to describe how large language model (LLM) performance scales with model size, training data size, or amount of computational resources. Motivated by th…
A convergent finite element method for two-phase Stokes flow driven by surface tension
Genming Bai, Harald Garcke, Shravan Veerapaneni
We present the first convergence proof for an iso-parametric finite element discretization of two-phase Stokes flow in , , with interface dynamics gov…
Convergence analysis for the Barrett--Garcke--Nurnberg method of transport type for evolving curves
Genming Bai, Harald Garcke, Shravan Veerapaneni
In this paper, we propose a Barrett-Garcke-Nurnberg (BGN) method for evolving geometries under general flows and present the corresponding convergence analysis. Specifically, we ex…
Enhancing the Clique Local Decoder to Correct Length-2 Space Errors in the Surface Code
Zikang Jia, Shravan Veerapaneni, Gokul Subramanian Ravi
The growing demand for fault-tolerant quantum computing drives the need for efficient, scalable Quantum Error Correction (QEC) strategies. Conventional decoders designed for worst-…
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…
Retentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry
Oliver Knitter, Dan Zhao, James Stokes +3
Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative…
An application of continuous-variable gate synthesis to quantum simulation of classical dynamics
Sam Cochran, James Stokes, Paramsothy Jayakumar +1
Although quantum computing holds promise to accelerate a wide range of computational tasks, the quantum simulation of quantum dynamics as originally envisaged by Feynman remains th…
Shape optimization of slip-driven axisymmetric microswimmers
Ruowen Liu, Hai Zhu, Hanliang Guo +2
In this work, we develop a computational framework that aims at simultaneously optimizing the shape and the slip velocity of an axisymmetric microswimmer suspended in a viscous flu…
Quantum-inspired nonlinear Galerkin ansatz for high-dimensional HJB equations
Chuhao Sun, Asaf Cohen, James Stokes +1
Neural networks are increasingly recognized as a powerful numerical solution technique for partial differential equations (PDEs) arising in diverse scientific computing domains, in…
Digital quantum simulation of Schrödinger dynamics using adaptive approximations of potential functions
Tenzan Araki, James Stokes, Shravan Veerapaneni
Digital quantum simulation (DQS) of continuous-variable quantum systems in the position basis requires efficient implementation of diagonal unitaries approximating the time evoluti…
Toward Neural Network Simulation of Variational Quantum Algorithms
Oliver Knitter, James Stokes, Shravan Veerapaneni
Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization. Desp…
An Incremental Tensor Train Decomposition Algorithm
Doruk Aksoy, David J. Gorsich, Shravan Veerapaneni +1
We present a new algorithm for incrementally updating the tensor train decomposition of a stream of tensor data. This new algorithm, called the {\em tensor train incremental core e…
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…
Scalable neural quantum states architecture for quantum chemistry
Tianchen Zhao, James Stokes, Shravan Veerapaneni
Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems. Despite rapid developments, sign…
Quantum-inspired variational algorithms for partial differential equations: Application to financial derivative pricing
Tianchen Zhao, Chuhao Sun, Asaf Cohen +2
Variational quantum Monte Carlo (VMC) combined with neural-network quantum states offers a novel angle of attack on the curse-of-dimensionality encountered in a particular class of…
Numerical and geometrical aspects of flow-based variational quantum Monte Carlo
James Stokes, Brian Chen, Shravan Veerapaneni
This article aims to summarize recent and ongoing efforts to simulate continuous-variable quantum systems using flow-based variational quantum Monte Carlo techniques, focusing for…
A fast, high-order scheme for evaluating volume potentials on complex 2D geometries via area-to-line integral conversion and domain mappings
Thomas G. Anderson, Hai Zhu, Shravan Veerapaneni
This article presents a new high-order accurate algorithm for finding a particular solution to a linear, constant-coefficient partial differential equation (PDE) by means of a conv…