• Open Access

Quantum Gram-Schmidt processes and their application to efficient state readout for quantum algorithms

Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, and Dacheng Tao
Phys. Rev. Research 3, 043095 – Published 1 November 2021

Abstract

Many quantum algorithms that claim speedup over their classical counterparts only generates quantum states as solutions instead of their final classical description. The additional step to decode quantum states into classical vectors normally will destroy the quantum advantage in most scenarios because all existing tomographic methods require runtime that is polynomial with respect to the state dimension. In this work, we present an efficient readout protocol that yields the classical vector form of the generated state, so it will achieve the end-to-end advantage for those quantum algorithms. Our protocol suits the case in which the output state lies in the row space of the input matrix, of rank r, that is stored in the quantum random access memory. The quantum resources for decoding the state in 2 norm with ε error require poly(r,1/ε) copies of the output state and poly(r,κr,1/ε) queries to the input oracles, where κ is the condition number of the input matrix. With our readout protocol, we completely characterize the end-to-end resources for quantum linear equation solvers and quantum singular value decomposition. One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure, which we believe will be of independent interest.

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  • Received 15 April 2020
  • Revised 17 August 2021
  • Accepted 29 September 2021

DOI:https://doi.org/10.1103/PhysRevResearch.3.043095

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Kaining Zhang1, Min-Hsiu Hsieh2, Liu Liu1, and Dacheng Tao1

  • 1School of Computer Science, Faculty of Engineering, University of Sydney, Australia
  • 2Hon Hai Quantum Computing Research Center, Taipei, Taiwan

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Issue

Vol. 3, Iss. 4 — November - December 2021

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