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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:
 [1];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1572797
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
EPJ Web of Conferences
Additional Journal Information:
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.
Shapoval, Illya, & Calafiura, Paolo. Quantum Associative Memory in Hep Track Pattern Recognition. United States. doi:10.1051/epjconf/201921401012.
Shapoval, Illya, and Calafiura, Paolo. Tue . "Quantum Associative Memory in Hep Track Pattern Recognition". United States. doi:10.1051/epjconf/201921401012. https://www.osti.gov/servlets/purl/1572797.
@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},
number = ,
volume = 214,
place = {United States},
year = {2019},
month = {9}
}

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Works referenced in this record:

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

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

Quantum associative memory
journal, May 2000


Quantum associative memory with distributed queries
journal, October 2000


Probabilistic Quantum Memories
journal, July 2001


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

QUANTUM COMPUTING:Beyond Factorization and Search
journal, August 1998


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


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


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