A quantum information theoretic view on a deep quantum neural network

Beatrix C. Hiesmayr (Corresponding author)

Publications: Contribution to journalMeeting abstract/Conference paperPeer Reviewed


We discuss a quantum version of an artificial deep neural network where the role of neurons is taken over by qubits and the role of weights is played by unitaries. The role of the non-linear activation function is taken over by subsequently tracing out layers (qubits) of the network. We study two examples and discuss the learning from a quantum information theoretic point of view. In detail, we show that the lower bound of the Heisenberg uncertainty relations is defining the change of the gradient descent in the learning process. We raise the question if the limit by Nature to two non-commuting observables, quantified in the Heisenberg uncertainty relations, is ruling the optimization of the quantum deep neural network. We find a negative answer.
Original languageEnglish
Article number020001
Number of pages9
JournalAIP Conference Proceedings
Issue number1
Publication statusPublished - 15 Mar 2024
Event2022 International Workshop on Machine Learning and Quantum Computing Applications in Medicine and Physics, WMLQ 2022 - Warsaw, Poland
Duration: 13 Sep 202216 Sep 2022

Austrian Fields of Science 2012

  • 103025 Quantum mechanics

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