arXiv subjects

Venkatesh Kannan

Publications and source records attributed to Venkatesh Kannan.

4 recordsLinked to original sources

arXiv2026

Towards Advancing Research with Workflows: A perspective from the Workflows Community Summit -- Amsterdam, 2025

Scientific workflows have become essential for orchestrating complex computational processes across distributed resources, managing large datasets, and ensuring reproducibility in modern research. The Workflows Community Summit 2025, held in Amsterdam on June 6th, 2025, convened international experts to examine emerging challenges and opportunities in this domain. Participants identified key barriers to workflow adoption, including tensions between system generality and domain-specific utility, concerns over long-term sustainability of workflow systems and services, insufficient recognition for those who develop and maintain reproducible workflows, and gaps in standardization, funding, training, and cross-disciplinary collaboration. To address these challenges, the summit proposed action lines spanning technology, policy, and community dimensions: shifting evaluation metrics from raw computational performance toward measuring genuine scientific impact; formalizing workflow patterns and community-driven benchmarks to improve transparency, reproducibility, and usability; cultivating a cohesive international workflows community that engages funding bodies and research stakeholders; and investing in human capital through dedicated workflow engineering roles, career pathways, and integration of workflow concepts into educational curricula and long-term training initiatives. This document presents the summit's findings, beginning with an overview of the current computing ecosystem and the rationale for workflow-centric approaches, followed by a discussion of identified challenges and recommended action lines for advancing scientific discovery through workflows.

cs.DC↗

arXiv2023

Pushing the Limits of Quantum Computing for Simulating PFAS Chemistry

Accurate and scalable methods for computational quantum chemistry can accelerate research and development in many fields, ranging from drug discovery to advanced material design. Solving the electronic Schrodinger equation is the core problem of computational chemistry. However, the combinatorial complexity of this problem makes it intractable to find exact solutions, except for very small systems. The idea of quantum computing originated from this computational challenge in simulating quantum-mechanics. We propose an end-to-end quantum chemistry pipeline based on the variational quantum eigensolver (VQE) algorithm and integrated with both HPC-based simulators and a trapped-ion quantum computer. Our platform orchestrates hundreds of simulation jobs on compute resources to efficiently complete a set of ab initio chemistry experiments with a wide range of parameterization. Per- and poly-fluoroalkyl substances (PFAS) are a large family of human-made chemicals that pose a major environmental and health issue globally. Our simulations includes breaking a Carbon-Fluorine bond in trifluoroacetic acid (TFA), a common PFAS chemical. This is a common pathway towards destruction and removal of PFAS. Molecules are modeled on both a quantum simulator and a trapped-ion quantum computer, specifically IonQ Aria. Using basic error mitigation techniques, the 11-qubit TFA model (56 entangling gates) on IonQ Aria yields near-quantitative results with milli-Hartree accuracy. Our novel results show the current state and future projections for quantum computing in solving the electronic structure problem, push the boundaries for the VQE algorithm and quantum computers, and facilitates development of quantum chemistry workflows.

quant-ph↗

arXiv2020

A hybrid classical-quantum workflow for natural language processing

Natural language processing (NLP) problems are ubiquitous in classical computing, where they often require significant computational resources to infer sentence meanings. With the appearance of quantum computing hardware and simulators, it is worth developing methods to examine such problems on these platforms. In this manuscript we demonstrate the use of quantum computing models to perform NLP tasks, where we represent corpus meanings, and perform comparisons between sentences of a given structure. We develop a hybrid workflow for representing small and large scale corpus data sets to be encoded, processed, and decoded using a quantum circuit model. In addition, we provide our results showing the efficacy of the method, and release our developed toolkit as an open software suite.

quant-ph↗

arXiv2016

Program Transformation to Identify List-Based Parallel Skeletons

Algorithmic skeletons are used as building-blocks to ease the task of parallel programming by abstracting the details of parallel implementation from the developer. Most existing libraries provide implementations of skeletons that are defined over flat data types such as lists or arrays. However, skeleton-based parallel programming is still very challenging as it requires intricate analysis of the underlying algorithm and often uses inefficient intermediate data structures. Further, the algorithmic structure of a given program may not match those of list-based skeletons. In this paper, we present a method to automatically transform any given program to one that is defined over a list and is more likely to contain instances of list-based skeletons. This facilitates the parallel execution of a transformed program using existing implementations of list-based parallel skeletons. Further, by using an existing transformation called distillation in conjunction with our method, we produce transformed programs that contain fewer inefficient intermediate data structures.

cs.PL↗