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The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline

Authors:

Christoph Roch,

Sebastian Feld,

Claudia Linnhoff-PopienAuthors Info & Claims

ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

Pages 456 - 461

https://doi.org/10.1145/3387940.3391469

Published: 25 September 2020 Publication History

Abstract

We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them to a formal model for machine learning processes, we deduce four major challenges for the future of quantum artificial intelligence: (i) Replace iterative training with faster quantum algorithms, (ii) distill the experience of larger amounts of data into the training process, (iii) allow quantum and classical components to be easily combined and exchanged, and (iv) build tools to thoroughly analyze whether observed benefits really stem from quantum properties of the algorithm.

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Cited By

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Kölle MStenzel GStein JZielinski SOmmer BLinnhoff-Popien C(2024)Quantum Denoising Diffusion Models2024 IEEE International Conference on Quantum Software (QSW)10.1109/QSW62656.2024.00023(88-98)Online publication date: 7-Jul-2024
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Zorn MStein JAltmann PKölle MLinnhoff-Popien CGabor T(2024)Cohesive Quantum Circuit Layer Construction with Reinforcement Learning2024 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE60285.2024.00201(1721-1730)Online publication date: 15-Sep-2024
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The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline

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cover image ACM Conferences

ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

June 2020

831 pages

ISBN:9781450379632

DOI:10.1145/3387940

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 25 September 2020

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Cited By

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https://doi.org/10.1134/S0361768824700804
Kölle MStenzel GStein JZielinski SOmmer BLinnhoff-Popien C(2024)Quantum Denoising Diffusion Models2024 IEEE International Conference on Quantum Software (QSW)10.1109/QSW62656.2024.00023(88-98)Online publication date: 7-Jul-2024
https://doi.org/10.1109/QSW62656.2024.00023
Zorn MStein JAltmann PKölle MLinnhoff-Popien CGabor T(2024)Cohesive Quantum Circuit Layer Construction with Reinforcement Learning2024 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE60285.2024.00201(1721-1730)Online publication date: 15-Sep-2024
https://doi.org/10.1109/QCE60285.2024.00201
Shah SVatsa M(2024)Enhancing Quantum Diffusion Models with Pairwise Bell State EntanglementPattern Recognition10.1007/978-3-031-78395-1_23(347-361)Online publication date: 3-Dec-2024
https://doi.org/10.1007/978-3-031-78395-1_23
Mešter M(2023)Potential of Quantum Technologies in the Energy Sector2023 23rd International Scientific Conference on Electric Power Engineering (EPE)10.1109/EPE58302.2023.10149255(1-6)Online publication date: 24-May-2023
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Weder BBarzen JBeisel MLeymann F(2023)Provenance-Preserving Analysis and Rewrite of Quantum Workflows for Hybrid Quantum AlgorithmsSN Computer Science10.1007/s42979-022-01625-94:3Online publication date: 23-Feb-2023
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Moret-Bonillo VMagaz-Romero SMosqueira-Rey E(2022)Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors ModelMathematics10.3390/math1002018910:2(189)Online publication date: 8-Jan-2022
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Gabor TZorn MLinnhoff-Popien CWagner M(2022)The applicability of reinforcement learning for the automatic generation of state preparation circuitsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534039(2196-2204)Online publication date: 9-Jul-2022
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Gabor TRosenfeld MLinnhoff-Popien CFeld S(2022)How to Approximate any Objective Function via Quadratic Unconstrained Binary Optimization2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00149(1249-1257)Online publication date: Mar-2022
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Gabor TLingsch Rosenfeld MLinnhoff-Popien C(2022)Simple Quantum State Encodings for Hybrid Programming of Quantum Simulators2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C)10.1109/ICSA-C54293.2022.00040(170-173)Online publication date: Mar-2022
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