NAPAC2022 - List of Authors (Brown, K.A.)

Paper	Title	Page
MOPA55	Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry	164
	J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov RadiaSoft LLC, Boulder, Colorado, USA K.A. Brown BNL, Upton, New York, USA S. Calder ORNL RAD, Oak Ridge, Tennessee, USA A.L. Edelen, B.D. O’Shea, R.J. Roussel SLAC, Menlo Park, California, USA C.M. Hoffmann ORNL, Oak Ridge, Tennessee, USA E.-C. Huang LANL, Los Alamos, New Mexico, USA P. Piot Northern Illinois University, DeKalb, Illinois, USA C. Tennant JLab, Newport News, Virginia, USA
	It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55
About •	Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE1	Coulomb Crystals in Storage Rings for Quantum Information Science	296
	K.A. Brown BNL, Upton, New York, USA A. Aslam, S. Biedron, T.B. Bolin, C. Gonzalez-Zacarias, S.I. Sosa Guitron UNM-ECE, Albuquerque, USA B. Huang SBU, Stony Brook, USA T.G. Robertazzi Stony Brook University, Stony Brook, New York, USA
	Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes.
	Slides TUYE1 [8.834 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE1
About •	Received ※ 17 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 11 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE1	Accelerators for Quantum Technologies
	A.S. Romanenko Fermilab, Batavia, Illinois, USA K.A. Brown BNL, Upton, New York, USA S. Sosa ODU, Norfolk, Virginia, USA
	Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.
	Slides THXE1 [7.433 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE2	Accelerators for Quantum Technologies
	K.A. Brown BNL, Upton, New York, USA
	Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.
	Slides THXE2 [6.768 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry

164

J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov
RadiaSoft LLC, Boulder, Colorado, USA
K.A. Brown
BNL, Upton, New York, USA
S. Calder
ORNL RAD, Oak Ridge, Tennessee, USA
A.L. Edelen, B.D. O’Shea, R.J. Roussel
SLAC, Menlo Park, California, USA
C.M. Hoffmann
ORNL, Oak Ridge, Tennessee, USA
E.-C. Huang
LANL, Los Alamos, New Mexico, USA
P. Piot
Northern Illinois University, DeKalb, Illinois, USA
C. Tennant
JLab, Newport News, Virginia, USA

It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55

About •

Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE1

Coulomb Crystals in Storage Rings for Quantum Information Science

296

K.A. Brown
BNL, Upton, New York, USA
A. Aslam, S. Biedron, T.B. Bolin, C. Gonzalez-Zacarias, S.I. Sosa Guitron
UNM-ECE, Albuquerque, USA
B. Huang
SBU, Stony Brook, USA
T.G. Robertazzi
Stony Brook University, Stony Brook, New York, USA

Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes.

Slides TUYE1 [8.834 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE1

About •

Received ※ 17 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 11 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE1

Accelerators for Quantum Technologies

A.S. Romanenko
Fermilab, Batavia, Illinois, USA
K.A. Brown
BNL, Upton, New York, USA
S. Sosa
ODU, Norfolk, Virginia, USA

Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.

Slides THXE1 [7.433 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE2

Accelerators for Quantum Technologies

K.A. Brown
BNL, Upton, New York, USA

Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.

Slides THXE2 [6.768 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)