Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

Thomas Hubregtsen, Christoph Segler, Josef Pichlmeier, Aritra Sarkar, Thomas Gabor, Koen Bertels

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attention of the current research is directed towards algorithmic research on artificial data that is disconnected from live systems, such as optimization of systems or training of learning algorithms. In this paper we investigate the integration of quantum systems into industry-grade system architectures. In this work we propose a system architecture for the integration of quantum accelerators. In order to evaluate our proposed system architecture we investigated various data-driven functions for various accelerators, including a classical system, a gate-based quantum accelerator and a quantum annealer. The data-driven function predict user preference and is trained on real-world data. This work also includes an evaluation of the quantum enhanced kernel, that previously was only evaluated on artificial data. In our evaluation, we showed that the quantum-enhanced kernel performs at least equally well to a classical state-of-The-Art kernel when simulated. We also showed a low reduction in accuracy and latency numbers within acceptable bounds when running on the gate-based IBM quantum accelerator. We therefore conclude it is feasible to integrate NISQ-era devices in industry-grade system architectures in preparation for future advancements in quantum hardware.

Original languageEnglish
Title of host publication2020 21st International Symposium on Quality Electronic Design (ISQED)
Subtitle of host publicationProceedings
PublisherIEEE
Pages329-334
Number of pages6
ISBN (Electronic)978-1-7281-4207-4
ISBN (Print)978-1-7281-4208-1
DOIs
Publication statusPublished - 2020
Event21st International Symposium on Quality Electronic Design, ISQED 2020 - Santa Clara, United States
Duration: 25 Mar 202026 Mar 2020

Conference

Conference21st International Symposium on Quality Electronic Design, ISQED 2020
CountryUnited States
CitySanta Clara
Period25/03/2026/03/20

Keywords

  • hybrid quantum/classical algorithms
  • quantum architectures
  • quantum devices
  • Quantum machine learning
  • quantum modelling

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    Hubregtsen, T., Segler, C., Pichlmeier, J., Sarkar, A., Gabor, T., & Bertels, K. (2020). Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions. In 2020 21st International Symposium on Quality Electronic Design (ISQED): Proceedings (pp. 329-334). [9137029] IEEE. https://doi.org/10.1109/ISQED48828.2020.9137029