Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor

W. Vlothuizen, J. F. Marques, J. van Straten, H. Ali, N. Muthusubramanian, C. Zachariadis, J. van Someren, M. Beekman, N. Haider, A. Bruno, C. G. Almudever, L. DiCarlo*, More Authors

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum circuits. In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions. These neurons constitute elementary building blocks that can be arranged in a variety of layouts to carry out deep learning tasks quantum coherently. As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions by optimization of network activation parameters within the supervised-learning paradigm. This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs.

Original languageEnglish
Article number118
Number of pages7
JournalNPJ Quantum Information
Volume9
Issue number1
DOIs
Publication statusPublished - 2023

Bibliographical note

Funding Information:
We thank G. Calusine and W. Oliver for providing the traveling-wave parametric amplifiers used in the readout amplification chain. This research is supported by Intel Corporation and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the U.S. Army Research Office Grant No. W911NF-16-1-0071. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government.

Publisher Copyright:
© 2023, The Author(s).

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