Characterizing the memory capacity of transmon qubit reservoirs

Samudra Dasgupta; Kathleen E. Hamilton; Arnab Banerjee

doi:10.1109/QCE53715.2022.00035

Characterizing the memory capacity of transmon qubit reservoirs

Samudra Dasgupta, Kathleen E. Hamilton, Arnab Banerjee

Quantum Computational Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100 to 500 nodes. Unlike traditional neural networks, we do not understand the guiding principles of reservoir design for high-performance information processing. Understanding the memory capacity of quantum reservoirs continues to be an open question. In this study, we focus on the task of characterizing the memory capacity of quantum reservoirs built using transmon devices provided by IBM. Our hybrid reservoir achieved a Normalized Mean Square Error (NMSE) of 6 × 10-4 for the NARMA (Non-linear Auto-regressive Moving Average) task which is comparable to recent benchmarks. The Memory Capacity characterization of a n-qubit reservoir showed a systematic variation with the complexity of the topology and exhibited a peak for the configuration with n - 1 self-loops. Such a peak provides a basis for selecting the optimal design for forecasting tasks.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	162-166
Number of pages	5
ISBN (Electronic)	9781665491136
DOIs	https://doi.org/10.1109/QCE53715.2022.00035
State	Published - 2022
Event	3rd IEEE International Conference on Quantum Computing and Engineering, QCE 2022 - Broomfield, United States Duration: Sep 18 2022 → Sep 23 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022

Conference

Conference	3rd IEEE International Conference on Quantum Computing and Engineering, QCE 2022
Country/Territory	United States
City	Broomfield
Period	09/18/22 → 09/23/22

Funding

ACKNOWLEDGEMENTS This research used quantum computing resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This work was partially supported as part of the ASCR QCAT program at Oak Ridge National Laboratory under FWP #ERKJ347. This work was partially supported as part of the ASCR Fundamental Algorithmic Research for Quantum Computing Program at Oak Ridge National Laboratory under FWP ERKJ354. Part of the support for SD and AB came from College of Science, Purdue University. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Data Science
Memory Capacity
Quantum Reservoir Computing
Time-series forecasting

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/QCE53715.2022.00035

Cite this

Dasgupta, S., Hamilton, K. E., & Banerjee, A. (2022). Characterizing the memory capacity of transmon qubit reservoirs. In Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022 (pp. 162-166). (Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QCE53715.2022.00035

Dasgupta, Samudra ; Hamilton, Kathleen E. ; Banerjee, Arnab. / Characterizing the memory capacity of transmon qubit reservoirs. Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 162-166 (Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022).

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abstract = "Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100 to 500 nodes. Unlike traditional neural networks, we do not understand the guiding principles of reservoir design for high-performance information processing. Understanding the memory capacity of quantum reservoirs continues to be an open question. In this study, we focus on the task of characterizing the memory capacity of quantum reservoirs built using transmon devices provided by IBM. Our hybrid reservoir achieved a Normalized Mean Square Error (NMSE) of 6 × 10-4 for the NARMA (Non-linear Auto-regressive Moving Average) task which is comparable to recent benchmarks. The Memory Capacity characterization of a n-qubit reservoir showed a systematic variation with the complexity of the topology and exhibited a peak for the configuration with n - 1 self-loops. Such a peak provides a basis for selecting the optimal design for forecasting tasks.",

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Dasgupta, S, Hamilton, KE & Banerjee, A 2022, Characterizing the memory capacity of transmon qubit reservoirs. in Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022. Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022, Institute of Electrical and Electronics Engineers Inc., pp. 162-166, 3rd IEEE International Conference on Quantum Computing and Engineering, QCE 2022, Broomfield, United States, 09/18/22. https://doi.org/10.1109/QCE53715.2022.00035

Characterizing the memory capacity of transmon qubit reservoirs. / Dasgupta, Samudra; Hamilton, Kathleen E.; Banerjee, Arnab.
Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 162-166 (Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2022

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N2 - Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100 to 500 nodes. Unlike traditional neural networks, we do not understand the guiding principles of reservoir design for high-performance information processing. Understanding the memory capacity of quantum reservoirs continues to be an open question. In this study, we focus on the task of characterizing the memory capacity of quantum reservoirs built using transmon devices provided by IBM. Our hybrid reservoir achieved a Normalized Mean Square Error (NMSE) of 6 × 10-4 for the NARMA (Non-linear Auto-regressive Moving Average) task which is comparable to recent benchmarks. The Memory Capacity characterization of a n-qubit reservoir showed a systematic variation with the complexity of the topology and exhibited a peak for the configuration with n - 1 self-loops. Such a peak provides a basis for selecting the optimal design for forecasting tasks.

AB - Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100 to 500 nodes. Unlike traditional neural networks, we do not understand the guiding principles of reservoir design for high-performance information processing. Understanding the memory capacity of quantum reservoirs continues to be an open question. In this study, we focus on the task of characterizing the memory capacity of quantum reservoirs built using transmon devices provided by IBM. Our hybrid reservoir achieved a Normalized Mean Square Error (NMSE) of 6 × 10-4 for the NARMA (Non-linear Auto-regressive Moving Average) task which is comparable to recent benchmarks. The Memory Capacity characterization of a n-qubit reservoir showed a systematic variation with the complexity of the topology and exhibited a peak for the configuration with n - 1 self-loops. Such a peak provides a basis for selecting the optimal design for forecasting tasks.

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Dasgupta S, Hamilton KE, Banerjee A. Characterizing the memory capacity of transmon qubit reservoirs. In Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 162-166. (Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022). doi: 10.1109/QCE53715.2022.00035

Characterizing the memory capacity of transmon qubit reservoirs

Abstract

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Keywords

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