Analog and hybrid computing in the 21st century
Abstract
Analog computers solve problems through physical representations of mathematical variables and relations, often using the dynamics of electronic circuits and their associated signals. Many analog computers directly represent nonlinear dynamical systems; they encode their solutions in the signals that they generate. Although most modern scientific computing relies on digital architectures with inherently discrete-valued representations and algorithmic approaches, the design constraints of such architectures increasingly limit both performance and energy efficiency. Analog systems can circumvent some of these constraints; they execute specific computations very efficiently by mapping them directly onto physical processes using smaller and simpler computing elements than the ones in digital architectures. In this article, we provide an interdisciplinary perspective on modern analog and hybrid (i.e., analog-digital) computing. We discuss how modern scientific computing workflows can leverage analog approaches in various applications, such as in simulating dynamical systems, performing optimization, and enabling more energy-efficient implementations of artificial intelligence.
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BibTeX
@misc{ulmann2026analog,
title = {{Analog and hybrid computing in the 21st century}},
author = {Bernd Ulmann and Olivier Bournez and Corey J. Maley and Zhong Sun and Grace Li Zhang and Paul Manea and Michael Döll and Frank Delvigne and Alex J. H. Fedorec and Daniel Garza and Lucas Wetzel and Sara Obergassel and Pavol Pitonak and Christopher Jung and Daniel Thuerck and Mason A. Porter and Marcelo J. Rozenberg and Frederic Bergner and Bhaskar Choubey and Manyu Zhao and Ludovico Minati and Lucas Böttcher},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00446},
eprint = {2611.00446},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00446},
}
RIS
TY - UNPB
TI - Analog and hybrid computing in the 21st century
AU - Bernd Ulmann
AU - Olivier Bournez
AU - Corey J. Maley
AU - Zhong Sun
AU - Grace Li Zhang
AU - Paul Manea
AU - Michael Döll
AU - Frank Delvigne
AU - Alex J. H. Fedorec
AU - Daniel Garza
AU - Lucas Wetzel
AU - Sara Obergassel
AU - Pavol Pitonak
AU - Christopher Jung
AU - Daniel Thuerck
AU - Mason A. Porter
AU - Marcelo J. Rozenberg
AU - Frederic Bergner
AU - Bhaskar Choubey
AU - Manyu Zhao
AU - Ludovico Minati
AU - Lucas Böttcher
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00446
UR - https://arxiv.org/abs/2611.00446
AB - Analog computers solve problems through physical representations of mathematical variables and relations, often using the dynamics of electronic circuits and their associated signals. Many analog computers directly represent nonlinear dynamical systems; they encode their solutions in the signals that they generate. Although most modern scientific computing relies on digital architectures with inherently discrete-valued representations and algorithmic approaches, the design constraints of such architectures increasingly limit both performance and energy efficiency. Analog systems can circumvent some of these constraints; they execute specific computations very efficiently by mapping them directly onto physical processes using smaller and simpler computing elements than the ones in digital architectures. In this article, we provide an interdisciplinary perspective on modern analog and hybrid (i.e., analog-digital) computing. We discuss how modern scientific computing workflows can leverage analog approaches in various applications, such as in simulating dynamical systems, performing optimization, and enabling more energy-efficient implementations of artificial intelligence.
ER -