10 citations · 31 across the 14 of their papers we have counts for
15 papers
Trapped-ion quantum simulation of the Fermi-Hubbard model as a lattice gauge theory using hardware-aware native gates
Dhruv Srinivasan, Alex Beyer, Daiwei Zhu +8
The Fermi-Hubbard model (FHM) is a simple yet rich model of strongly interacting electrons with complex dynamics and a variety of emerging quantum phases. These properties make it…
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates
Kushal Chakrabarti, Mayank Baranwal
Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretization. We introduce a second-order primal--du…
A Methodology Establishing Linear Convergence of Adaptive Gradient Methods under PL Inequality
Kushal Chakrabarti, Mayank Baranwal
Adaptive gradient-descent optimizers are the standard choice for training neural network models. Despite their faster convergence than gradient-descent and remarkable performance i…
On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer
Kushal Chakrabarti, Nikhil Chopra
This paper considers the observer design problem for discrete-time nonlinear dynamical systems with sampled measurement data. Earlier, the recently proposed Iteratively Preconditio…
On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality
Kushal Chakrabarti, Mayank Baranwal
This paper considers solving distributed optimization problems in peer-to-peer multi-agent networks. The network is synchronous and connected. By using the proportional-integral (P…
Quantum Circuit Optimization through Iteratively Pre-Conditioned Gradient Descent
Dhruv Srinivasan, Kushal Chakrabarti, Nikhil Chopra +1
For typical quantum subroutines in the gate-based model of quantum computing, explicit decompositions of circuits in terms of single-qubit and two-qubit entangling gates may exist.…
Iteratively Preconditioned Gradient-Descent Approach for Moving Horizon Estimation Problems
Tianchen Liu, Kushal Chakrabarti, Nikhil Chopra
Moving horizon estimation (MHE) is a widely studied state estimation approach in several practical applications. In the MHE problem, the state estimates are obtained via the soluti…
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
Kushal Chakrabarti, Nikhil Chopra
Adaptive gradient methods have become popular in optimizing deep neural networks; recent examples include AdaGrad and Adam. Although Adam usually converges faster, variations of Ad…
On Accelerating Distributed Convex Optimizations
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper studies a distributed multi-agent convex optimization problem. The system comprises multiple agents in this problem, each with a set of local data points and an associat…
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective
Kushal Chakrabarti, Nikhil Chopra
Accelerated gradient-based methods are being extensively used for solving non-convex machine learning problems, especially when the data points are abundant or the available data i…
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the problem of multi-agent distributed linear regression in the presence of system noises. In this problem, the system comprises multiple agents wherein each a…
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the multi-agent distributed linear least-squares problem. The system comprises multiple agents, each agent with a locally observed set of data points, and a co…
Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the multi-agent linear least-squares problem in a server-agent network. In this problem, the system comprises multiple agents, each having a set of local data…
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the problem of multi-agent distributed optimization. In this problem, there are multiple agents in the system, and each agent only knows its local cost functio…
A Kalman Filter Approach for Biomolecular Systems with Noise Covariance Updating
Abhishek Dey, Kushal Chakrabarti, Krishan Kumar Gola +1
An important part of system modeling is determining parameter values, particularly for biomolecular systems, where direct measurements of individual parameters are typically hard.…