Tensor Product Approach to Quantum Control

Diego A. Quinones Valles, Sergey Dolgov, Dmitry Savostyanov

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN


In this proof-of-concept paper we show that tensor product approach is efficient for control of large quantum systems, such as Heisenberg spin wires, which are essential for emerging quantum computing technologies. We compute optimal control sequences using GRAPE method, applying the recently developed tAMEn algorithm to calculate evolution of quantum states represented in the tensor train format to reduce storage. Using tensor product algorithms we can overcome the curse of dimensionality and compute the optimal control pulse for a 41 spin system on a single workstation with fully controlled accuracy and huge savings of computational time and memory. The use of tensor product algorithms opens new approaches for development of quantum computers with 50–100 qubits.
Original languageEnglish
Title of host publicationIntegral Methods in Science and Engineering
EditorsCristian Constanda, Paul J Harris
ISBN (Electronic)9783030160777
ISBN (Print)9783030160760
Publication statusPublished - 19 Jul 2019


  • quantum control
  • high-dimensional problems
  • optimisation
  • tensor product approximations
  • algorithmic synthesis of quantum circuits

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  • Activities

    • 1 Oral presentation

    Tensor product approach to quantum control

    Dmitry Savostyanov (Presenter)

    26 Sep 201927 Sep 2019

    Activity: External talk or presentationOral presentation


    Cite this

    Quinones Valles, D. A., Dolgov, S., & Savostyanov, D. (2019). Tensor Product Approach to Quantum Control. In C. Constanda, & P. J. Harris (Eds.), Integral Methods in Science and Engineering (pp. 367-379). Birkhäuser. https://doi.org/10.1007/978-3-030-16077-7_29