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

Journal Article (2023)
Author(s)

W. J. Vlothuizen (TU Delft - QuTech Advanced Research Centre, TU Delft - BUS/Quantum Delft)

J.M. Ferreira Marques (TU Delft - QCD/DiCarlo Lab, Kavli institute of nanoscience Delft)

Jeroen Van Straten (TU Delft - QuTech Advanced Research Centre, TU Delft - BUS/Quantum Delft)

H. Ali (TU Delft - Building Operations, Kavli institute of nanoscience Delft, TU Delft - QuTech Advanced Research Centre)

N. Muthusubramanian (Kavli institute of nanoscience Delft, TU Delft - QuTech Advanced Research Centre, TU Delft - Communication QuTech, TU Delft - QN/Kavli Nanolab Delft)

C. Zachariadis (TU Delft - QuTech Advanced Research Centre, TU Delft - QN/Kavli Nanolab Delft, Kavli institute of nanoscience Delft)

J. van Someren (TU Delft - QCD/Feld Group, TU Delft - QuTech Advanced Research Centre)

M. Beekman (TNO, TU Delft - Codesigning Social Change, TU Delft - QuTech Advanced Research Centre)

N. Haider (TNO, TU Delft - QuTech Advanced Research Centre, TU Delft - Microwave Sensing, Signals & Systems, DIANA FEA )

A Bruno (Kavli institute of nanoscience Delft, TU Delft - QN/Kavli Nanolab Delft)

Carmen Garcia García Almudever (QCD/Sebastiano Lab, TU Delft - QuTech Advanced Research Centre)

L di Carlo (TU Delft - QN/DiCarlo Lab, TU Delft - QuTech Advanced Research Centre, TU Delft - QCD/DiCarlo Lab, Kavli institute of nanoscience Delft)

More Authors (External organisation)

Research Group
BUS/Quantum Delft
Copyright
© 2023 W.J. Vlothuizen, J.M. Ferreira Marques, J. van Straten, H. Ali, N. Muthusubramanian, C. Zachariadis, J. van Someren, M. Beekman, S.N. Haider, A. Bruno, Carmen G. Almudever, L. DiCarlo, More Authors
DOI related publication
https://doi.org/10.1038/s41534-023-00779-5
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 W.J. Vlothuizen, J.M. Ferreira Marques, J. van Straten, H. Ali, N. Muthusubramanian, C. Zachariadis, J. van Someren, M. Beekman, S.N. Haider, A. Bruno, Carmen G. Almudever, L. DiCarlo, More Authors
Research Group
BUS/Quantum Delft
Issue number
1
Volume number
9
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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.