6 papers
Prediction of Runtime Parameters of Parallel Chemistry Applications via Active and Generative Learning
Tanzila Tabassum, Omer Subasi, Ajay Panyala +5
In this work, we develop two main Machine Learning based approaches to predict the runtime parameters of highly scalable parallel chemistry computations.These approaches employ act…
Quantum Information Harvesting with the Parallel Quantum Flow Algorithm
Nicholas P. Bauman, Ajay Panyala, Chenxu Liu +3
The Quantum Flow (QFlow) algorithm provides a resource-efficient framework for describing correlated many-body systems on hybrid quantum-classical architectures. By enabling parall…
Guiding Application Users via Estimation of Computational Resources for Massively Parallel Chemistry Computations
Tanzila Tabassum, Omer Subasi, Ajay Panyala +6
In this work, we develop machine learning (ML) based strategies to predict resources (costs) required for massively parallel chemistry computations, such as coupled-cluster methods…
Integrated Software/Hardware Execution Models for High-Accuracy Methods in Chemistry
Nicholas Bauman, Ajay Panyala, Libor Veis +7
The effective deployment and application of advanced methodologies for quantum chemistry is inherently linked to the optimal usage of emerging and highly diversified computational…
Coupled Cluster Downfolding Theory in Simulations of Chemical Systems on Quantum Hardware
Nicholas P. Bauman, Muqing Zheng, Chenxu Liu +5
The practical application of quantum technologies to chemical problems faces significant challenges, particularly in the treatment of realistic basis sets and the accurate inclusio…
Exploring the exact limits of the real-time equation-of-motion coupled cluster cumulant Green's functions
Bo Peng, Himadri Pathak, Ajay Panyala +3
In this paper, we analyze the properties of the recently proposed real-time equation-of-motion coupled-cluster (RT-EOM-CC) cumulant Green's function approach [J. Chem. Phys. 2020,…