Print Email Facebook Twitter Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor Title Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor Author Vlothuizen, W.J. (TU Delft BUS/Quantum Delft; TU Delft QuTech Advanced Research Centre) Ferreira Marques, J.M. (TU Delft QCD/DiCarlo Lab; Kavli institute of nanoscience Delft) van Straten, J. (TU Delft BUS/Quantum Delft; TU Delft QuTech Advanced Research Centre) Ali, H. (TU Delft CRE Dagelijks Huurders Onderhoud; TU Delft QuTech Advanced Research Centre; Kavli institute of nanoscience Delft) Muthusubramanian, N. (TU Delft QN/Kavli Nanolab Delft; TU Delft Communication QuTech; TU Delft QuTech Advanced Research Centre; Kavli institute of nanoscience Delft) Zachariadis, C. (TU Delft QN/Kavli Nanolab Delft; TU Delft QuTech Advanced Research Centre; Kavli institute of nanoscience Delft) van Someren, J. (TU Delft QCD/Feld Group; TU Delft QuTech Advanced Research Centre) Beekman, M. (TU Delft Design Conceptualization and Communication; TU Delft QuTech Advanced Research Centre; TNO) Haider, S.N. (TU Delft Microwave Sensing, Signals & Systems; TU Delft QuTech Advanced Research Centre; DIANA FEA; TNO) Bruno, A. (TU Delft QN/Kavli Nanolab Delft; Kavli institute of nanoscience Delft) Almudever, Carmen G. (TU Delft QCD/Sebastiano Lab; TU Delft QuTech Advanced Research Centre) DiCarlo, L. (TU Delft QCD/DiCarlo Lab; TU Delft QN/DiCarlo Lab; TU Delft QuTech Advanced Research Centre; Kavli institute of nanoscience Delft) Date 2023 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. To reference this document use: http://resolver.tudelft.nl/uuid:aa4fe0b0-8834-4cd8-9397-71e8d60a27fd DOI https://doi.org/10.1038/s41534-023-00779-5 ISSN 2056-6387 Source NPJ Quantum Information, 9 (1) Part of collection Institutional Repository Document type journal article Rights © 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 Files PDF s41534_023_00779_5.pdf 1.79 MB Close viewer /islandora/object/uuid:aa4fe0b0-8834-4cd8-9397-71e8d60a27fd/datastream/OBJ/view