Quantum Associative Memory in Hep Track Pattern Recognition

Shapoval, Illya; Calafiura, Paolo

doi:10.1051/epjconf/201921401012

Title: Quantum Associative Memory in Hep Track Pattern Recognition

Abstract

We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.

Authors:

Shapoval, Illya ^[1]; Calafiura, Paolo ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Publication Date:: Tue Sep 17 00:00:00 EDT 2019

Research Org.:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), High Energy Physics (HEP)

OSTI Identifier:: 1572797

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: EPJ Web of Conferences (Online)

Additional Journal Information:: Journal Name: EPJ Web of Conferences (Online); Journal Volume: 214; Journal ID: ISSN 2100-014X

Publisher:: EDP Sciences

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Shapoval, Illya, and Calafiura, Paolo. Quantum Associative Memory in Hep Track Pattern Recognition.  United States: N. p., 2019. 
Web.  doi:10.1051/epjconf/201921401012.

Copy to clipboard


                    Shapoval, Illya, & Calafiura, Paolo. Quantum Associative Memory in Hep Track Pattern Recognition.  United States.  https://doi.org/10.1051/epjconf/201921401012

Copy to clipboard


                    Shapoval, Illya, and Calafiura, Paolo. Tue .  
"Quantum Associative Memory in Hep Track Pattern Recognition".  United States.  https://doi.org/10.1051/epjconf/201921401012.  https://www.osti.gov/servlets/purl/1572797.

Copy to clipboard


                    
@article{osti_1572797,

  title        = {Quantum Associative Memory in Hep Track Pattern Recognition},

  author       = {Shapoval, Illya and Calafiura, Paolo},

  abstractNote = {We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.},

  doi          = {10.1051/epjconf/201921401012},

  journal      = {EPJ Web of Conferences (Online)},

  number       = ,

  volume       = 214,

  place        = {United States},

  year         = {Tue Sep 17 00:00:00 EDT 2019},

  month        = {Tue Sep 17 00:00:00 EDT 2019}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1051/epjconf/201921401012

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Quantum associative memory with distributed queries
journal, October 2000

Ezhov, A. A.; Nifanova, A. V.; Ventura, Dan
Information Sciences, Vol. 128, Issue 3-4
DOI: 10.1016/S0020-0255(00)00057-8

QUANTUM COMPUTING:Beyond Factorization and Search
journal, August 1998

Grover, L. K.
Science, Vol. 281, Issue 5378
DOI: 10.1126/science.281.5378.792

A fast quantum mechanical algorithm for database search
preprint, January 1996

Grover, Lov K.
arXiv
DOI: 10.48550/arxiv.quant-ph/9605043

Grover’s quantum searching algorithm is optimal
journal, October 1999

Zalka, Christof
Physical Review A, Vol. 60, Issue 4
DOI: 10.1103/PhysRevA.60.2746

Grover’s quantum search algorithm for an arbitrary initial amplitude distribution
journal, October 1999

Biham, Eli; Biham, Ofer; Biron, David
Physical Review A, Vol. 60, Issue 4
DOI: 10.1103/PhysRevA.60.2742

Quantum Mechanics Helps in Searching for a Needle in a Haystack
journal, July 1997

Grover, Lov K.
Physical Review Letters, Vol. 79, Issue 2
DOI: 10.1103/PhysRevLett.79.325

Quantum associative memory
journal, May 2000

Ventura, D.
Information Sciences, Vol. 124, Issue 1-4
DOI: 10.1016/S0020-0255(99)00101-2

The AMchip: a full-custom CMOS VLSI associative memory for pattern recognition
journal, January 1992

Amendolia, S. R.; Galeotti, S.; Morsani, F.
IEEE Transactions on Nuclear Science, Vol. 39, Issue 4
DOI: 10.1109/23.159709

Probabilistic Quantum Memories
journal, July 2001

Trugenberger, C. A.
Physical Review Letters, Vol. 87, Issue 6
DOI: 10.1103/PhysRevLett.87.067901

Dense quantum coding and a lower bound for 1-way quantum automata
conference, January 1999

Ambainis, Andris; Nayak, Ashwin; Ta-Shma, Ammon
Proceedings of the thirty-first annual ACM symposium on Theory of computing - STOC '99
DOI: 10.1145/301250.301347

The AMchip: a VLSI associative memory for track finding
journal, May 1992

Amendolia, S. R.; Galeotti, S.; Morsani, F.
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 315, Issue 1-3
DOI: 10.1016/0168-9002(92)90744-O

Quantum Computation and Quantum Information
book, January 2011

Nielsen, Michael A.; Chuang, Isaac L.
Cambridge University Press
DOI: 10.1017/CBO9780511976667

Works referencing / citing this record:

Particle track classification using quantum associative memory
journal, September 2021

Quiroz, Gregory; Ice, Lauren; Delgado, Andrea
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1010
DOI: 10.1016/j.nima.2021.165557

Performance of Particle Tracking Using a Quantum Graph Neural Network
preprint, January 2020

Tüysüz, Cenk; Novotny, Kristiane; Rieger, Carla
arXiv
DOI: 10.48550/arxiv.2012.01379

Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research
preprint, January 2022

Humble, Travis S.; Delgado, Andrea; Pooser, Raphael
arXiv
DOI: 10.48550/arxiv.2203.07091

Quantum machine learning in high energy physics
journal, March 2021

Guan, Wen; Perdue, Gabriel; Pesah, Arthur
Machine Learning: Science and Technology, Vol. 2, Issue 1
DOI: 10.1088/2632-2153/abc17d

Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications
text, January 2021

Jang, Wonho; Terashi, Koji; Saito, Masahiko
arXiv
DOI: 10.48550/arxiv.2102.10008

Similar Records in DOE PAGES and OSTI.GOV collections:

Particle track classification using quantum associative memory

Journal Article Quiroz, Gregory ; Ice, Lauren ; Delgado, Andrea ; ... - Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment

Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrimination of relevant interactions, pattern recognition seeks to accelerate track reconstruction by isolating signals of interest. In high collision rate experiments, such algorithms can be particularly crucial for determining whether to retain or discard information from a given interaction even before the data is transferred to tape. As data rates, detector resolution, noise, and inefficiencies increase, pattern recognition becomes more computationally challenging, motivating the development of higher efficiency algorithms and techniques. Quantum associative memory is an approachmore »« less
https://doi.org/10.1016/j.nima.2021.165557

Full Text Available
Distributed-memory Parallel Algorithms for Matching and Coloring

Conference Catalyurek, Umit ; Dobrian, Florin ; Gebremedhin, Assefaw H ; ...

Graph matching and coloring constitute two fundamental classes of combinatorial problems having numerous established as well as emerging applications in computational science and engineering, high-performance computing, and informatics. We provide a snapshot of an on-going work on the design and implementation of new highly-scalable distributed-memory parallel algorithms for two prototypical problems from these classes, edge-weighted matching and distance-1 vertex coloring. Graph algorithms in general have low concurrency and poor data locality, making it challenging to achieve scalability on massively parallel machines. We overcome this challenge by employing a variety of techniques, including approximation, speculation and iteration, optimized communication, and randomization,more »« less
https://doi.org/10.1109/IPDPS.2011.360
Variational quantum eigensolver with reduced circuit complexity

Journal Article Zhang, Yu ; Cincio, Lukasz ; Negre, Christian F. A. ; ... - npj Quantum Information

Abstract The variational quantum eigensolver (VQE) is one of the most promising algorithms to find eigenstates of a given Hamiltonian on noisy intermediate-scale quantum devices (NISQ). The practical realization is limited by the complexity of quantum circuits. Here we present an approach to reduce quantum circuit complexity in VQE for electronic structure calculations. Our ClusterVQE algorithm splits the initial qubit space into clusters which are further distributed on individual (shallower) quantum circuits. The clusters are obtained based on mutual information reflecting maximal entanglement between qubits, whereas inter-cluster correlation is taken into account via a new “dressed” Hamiltonian. ClusterVQE therefore allowsmore »« less
https://doi.org/10.1038/s41534-022-00599-z
Quantum Computing: Advancing Fundamental Physics

Journal Article Spentzouris, Panagiotis - Computing and Software for Big Science

The field of Quantum Information Science (QIS) is a rapidly growing field, with an expanding number of potential applications that explore the capabilities of currently available noisy quantum devices and promise to eventually solve problems beyond anything that classical systems can accomplish. In the past few years, this expectation for transformational applications has translated to a lot of attention, both from the media (reaching the scientific community and the general public) and the government funding agencies and technology industry. Examples of this attention include the National Quantum Initiative in the USA that has already generated significant funding (e.g., $625 millionmore »« less
https://doi.org/10.1007/s41781-020-00043-x

Full Text Available
Particle Track Classification Using Quantum Associative Memory (Final Technical Report)

Technical Report Ice, Lauren Diane ; Quiroz, Gregory ; Delgado, Andrea ; ...

This project explored the use of quantum-assisted algorithms for pattern matching in sub-atomic physics experiments. Pattern matching algorithms are commonly employed to prune data of random noise and to help discriminate between signals generated by particle tracks of interest and signals generated by background events. The quantum-assisted algorithms explored in this project were based on an Ising formulation of quantum associative model (QAMM) recall and quantum content-addressable memory (QCAM) recall. The recall is performed by comparing a probe pattern with those stored in a library of patterns encoded in the QAMM/QCAM model. The classification accuracy of QAMM and QCAM recallmore »« less
https://doi.org/10.2172/1751912

Full Text Available

Similar Records

Title: Quantum Associative Memory in Hep Track Pattern Recognition

Abstract

Citation Formats

Quantum associative memory with distributed queries journal, October 2000

QUANTUM COMPUTING:Beyond Factorization and Search journal, August 1998

A fast quantum mechanical algorithm for database search preprint, January 1996

Grover’s quantum searching algorithm is optimal journal, October 1999

Grover’s quantum search algorithm for an arbitrary initial amplitude distribution journal, October 1999

Quantum Mechanics Helps in Searching for a Needle in a Haystack journal, July 1997

Quantum associative memory journal, May 2000

The AMchip: a full-custom CMOS VLSI associative memory for pattern recognition journal, January 1992

Probabilistic Quantum Memories journal, July 2001

Dense quantum coding and a lower bound for 1-way quantum automata conference, January 1999

The AMchip: a VLSI associative memory for track finding journal, May 1992

Quantum Computation and Quantum Information book, January 2011

Particle track classification using quantum associative memory journal, September 2021

Performance of Particle Tracking Using a Quantum Graph Neural Network preprint, January 2020

Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research preprint, January 2022

Quantum machine learning in high energy physics journal, March 2021

Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications text, January 2021

Quantum associative memory with distributed queries
journal, October 2000

QUANTUM COMPUTING:Beyond Factorization and Search
journal, August 1998

A fast quantum mechanical algorithm for database search
preprint, January 1996

Grover’s quantum searching algorithm is optimal
journal, October 1999

Grover’s quantum search algorithm for an arbitrary initial amplitude distribution
journal, October 1999

Quantum Mechanics Helps in Searching for a Needle in a Haystack
journal, July 1997

Quantum associative memory
journal, May 2000

The AMchip: a full-custom CMOS VLSI associative memory for pattern recognition
journal, January 1992

Probabilistic Quantum Memories
journal, July 2001

Dense quantum coding and a lower bound for 1-way quantum automata
conference, January 1999

The AMchip: a VLSI associative memory for track finding
journal, May 1992

Quantum Computation and Quantum Information
book, January 2011

Particle track classification using quantum associative memory
journal, September 2021

Performance of Particle Tracking Using a Quantum Graph Neural Network
preprint, January 2020

Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research
preprint, January 2022

Quantum machine learning in high energy physics
journal, March 2021

Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications
text, January 2021