Tensor Product Approach to Quantum Control

Diego Quiñones-Valles (), Sergey Dolgov () and Dmitry Savostyanov ()
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Diego Quiñones-Valles: University of Brighton
Sergey Dolgov: University of Bath
Dmitry Savostyanov: University of Brighton

Chapter Chapter 29 in Integral Methods in Science and Engineering, 2019, pp 367-379 from Springer

Abstract: Abstract 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-16077-7_29

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DOI: 10.1007/978-3-030-16077-7_29

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