Editorial: Towards Exascale Solutions for Big Data Computing

Talia, Domenico; Trunfio, Paolo; Carretero, J.; Blas, Javier García

doi:10.3389/fdata.2022.838097

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An increasing demand for computational capacity will be driven in the next decades by advancements in big data management and analysis, with massive consequences on the evolution of multiscale models. ,− This urgent requirement is already inducing a shift in how we perceive computation. The limitation imposed by the on-chip power dissipation of current semiconductor technologies is hindering the development of traditional processing architectures, and we may witness a deceleration of Moore’s law.…”

Section: State Of the Artmentioning

confidence: 99%

A Vision for the Future of Multiscale Modeling

Capone,

Romanelli,

Castaldo

et al. 2024

ACS Phys. Chem Au

View full text Add to dashboard Cite

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

show abstract

Section: State Of the Artmentioning

confidence: 99%

A Vision for the Future of Multiscale Modeling

Capone,

Romanelli,

Castaldo

et al. 2024

ACS Phys. Chem Au

View full text Add to dashboard Cite

show abstract

A differentiable quantum phase estimation algorithm

Castaldo,

Jahangiri,

Migliore

et al. 2024

Quantum Sci. Technol.

View full text Add to dashboard Cite

The simulation of electronic properties is a pivotal issue in modern electronic structure theory, driving significant efforts over the past decades to develop protocols for computing energy derivatives. In this work, we address this problem by developing a strategy to integrate the quantum phase estimation algorithm within a fully differentiable framework. This is accomplished by devising a smooth estimator able to tackle arbitrary initial states. We provide analytical expressions to characterize the statistics and algorithmic cost of this estimator. Furthermore, we provide numerical evidence that the estimation accuracy is retained when an arbitrary state is considered and that it exceeds the one of standard majority rule. We explicitly use this procedure to estimate chemically relevant quantities, demonstrating our approach through ground-state and triplet excited state geometry optimization with simulations involving up to 19 qubits. This work paves the way for new quantum algorithms that combine interference methods and quantum differentiable programming.

show abstract

Computational Medicine in the Exascale Computing Era: Applications at the Oak Ridge Leadership Computing Facility

Tourassi

2023

2023 IEEE John Vincent Atanasoff International Symposium on Modern Computing (JVA)

View full text Add to dashboard Cite

Editorial: Towards Exascale Solutions for Big Data Computing

Cited by 3 publications

References 4 publications

A Vision for the Future of Multiscale Modeling

A Vision for the Future of Multiscale Modeling

A differentiable quantum phase estimation algorithm

Computational Medicine in the Exascale Computing Era: Applications at the Oak Ridge Leadership Computing Facility

Contact Info

Product

Resources

About