Citation

BibTex format

@article{Gonski:2026:10.1103/bjx3-vlj8,
author = {Gonski, J and Ott, J and Abbaszadeh, S and Addepalli, S and Cremonesi, M and Dickinson, J and Di, Guglielmo G and Ertorer, YE and Gray, L and Herbst, R and Herwig, C and Hong, TM and Maier, B and Makou, MB and Miller, DW and Neubauer, MS and Peña, C and Rankin, D and Seo, S-H and Stark, G and Tapper, A and Therrien, AC and Xiotidis, I and Yoshihara, K and Akchurin, N and Argüelles, C and Bhattacharya, S and Borella, L and Boutan, C and Braine, T and Brau, J and Carminati, M and Chahine, A and Chou, Y-T and Chowdhury, TA and Chung, S and Coffee, R and Coppi, A and DAlfonso, M and Dave, A and Desmet, C and Di, Fulvio A and DiPetrillo, K and Duarte, J and Edelen, A and Eysermans, J and Feng, Y and Forrestel, E and García, Pardiñas J and Gastaldo, L and Gerlach, L and Gnaneswaran, A and Gouskos, L and Govorkova, K and Grace, C and Grant, C and Harris, P and Hasnip, C and Heim, T and Holtermann, A and Innocenti, GM and Ishidoshiro, K and Jin, M and Johnson, J and Jones, S and Jung, A and Ka},
doi = {10.1103/bjx3-vlj8},
journal = {PRX Intelligence},
title = {Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics},
url = {http://dx.doi.org/10.1103/bjx3-vlj8},
volume = {1},
year = {2026}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:p>The next generation of particle physics experiments will face an era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing these data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning, silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing toward a successful transition to the data frontier of fundamental science.</jats:p>
AU  - Gonski,J
AU  - Ott,J
AU  - Abbaszadeh,S
AU  - Addepalli,S
AU  - Cremonesi,M
AU  - Dickinson,J
AU  - Di,Guglielmo G
AU  - Ertorer,YE
AU  - Gray,L
AU  - Herbst,R
AU  - Herwig,C
AU  - Hong,TM
AU  - Maier,B
AU  - Makou,MB
AU  - Miller,DW
AU  - Neubauer,MS
AU  - Peña,C
AU  - Rankin,D
AU  - Seo,S-H
AU  - Stark,G
AU  - Tapper,A
AU  - Therrien,AC
AU  - Xiotidis,I
AU  - Yoshihara,K
AU  - Akchurin,N
AU  - Argüelles,C
AU  - Bhattacharya,S
AU  - Borella,L
AU  - Boutan,C
AU  - Braine,T
AU  - Brau,J
AU  - Carminati,M
AU  - Chahine,A
AU  - Chou,Y-T
AU  - Chowdhury,TA
AU  - Chung,S
AU  - Coffee,R
AU  - Coppi,A
AU  - DAlfonso,M
AU  - Dave,A
AU  - Desmet,C
AU  - Di,Fulvio A
AU  - DiPetrillo,K
AU  - Duarte,J
AU  - Edelen,A
AU  - Eysermans,J
AU  - Feng,Y
AU  - Forrestel,E
AU  - García,Pardiñas J
AU  - Gastaldo,L
AU  - Gerlach,L
AU  - Gnaneswaran,A
AU  - Gouskos,L
AU  - Govorkova,K
AU  - Grace,C
AU  - Grant,C
AU  - Harris,P
AU  - Hasnip,C
AU  - Heim,T
AU  - Holtermann,A
AU  - Innocenti,GM
AU  - Ishidoshiro,K
AU  - Jin,M
AU  - Johnson,J
AU  - Jones,S
AU  - Jung,A
AU  - Kamp,N
AU  - Karagiorgi,G
AU  - Kastner,R
AU  - Kim,D
AU  - Kong,K
AU  - Kudela,K
AU  - Lai,B-C
AU  - Lai,Y-T
AU  - Lalic,J
AU  - Lam,T
AU  - Lazar,J
AU  - Li,A
AU  - Li,Z
AU  - Liu,H
AU  - Liu,Q
AU  - Lonar,V
AU  - Ma,Z
AU  - Macchiarulo,L
AU  - Madrid,C
AU  - Mukim,P
AU  - Nguyen,V
AU  - Oh,S
AU  - Ojalvo,I
AU  - Ozaki,H
AU  - Pagan,Griso S
AU  - Parajuli,S
AU  - Park,M
AU  - Parpillon,B
AU  - Paus,C
AU  - Pozzi,S
AU  - Puljak,E
AU  - Ramhorst,B
AU  - Roberts,A
AU  - Ruckman,L
AU  - Schmitt,S
AU  - Scholberg,K
AU  - Singer,N
AU  - Smith,EA
AU  - Sousa,A
AU  - Spannowsky,M
AU  - Sugizaki,K
AU  - Summers,S
AU  - Sun,Y
AU  - Takaki,DT
AU  - Tumeo,A
AU  - Vernieri,C
AU  - von,Krosigk B
AU  - Vora,Y
AU  - Wan,L
AU  - Wang,MHLS
AU  - Weinstein,A
AU  - White,A
AU  - Williams,S
AU  - Wu,L
AU  - Yu,F
DO  - 10.1103/bjx3-vlj8
PY  - 2026///
TI  - Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics
T2  - PRX Intelligence
UR  - http://dx.doi.org/10.1103/bjx3-vlj8
UR  - https://doi.org/10.1103/bjx3-vlj8
VL  - 1
ER  -
Download