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Improvements in Quantum SDP-Solving with Applications

Authors Joran van Apeldoorn, András Gilyén

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Author Details

Joran van Apeldoorn
  • QuSoft, CWI, The Netherlands
András Gilyén
  • QuSoft, CWI, The Netherlands

Funding

  • van Apeldoorn, Joran: Supported by the Netherlands Organization for Scientific Research, grant number 617.001.351.
  • Gilyén, András: Supported by ERC Consolidator Grant 615307-QPROGRESS and partially supported by QuantERA project QuantAlgo 680-91-034.

Acknowledgements

We thank the authors of [Fernando G. S. L. Brandão et al., 2018] for sending work-in-progress versions of their paper, and Fernando Brãndao, Tongyang Li and Xiaodi Wu for personal communication. A.G. thanks Robin Kothari and Nathan Wiebe for useful discussions. We thank Jamie Sikora for useful discussions about applications and for suggesting the state discrimination problem. We are grateful to Ronald de Wolf and Sander Gribling for useful discussions, and advice about the manuscript.

Cite As Get BibTex

Joran van Apeldoorn and András Gilyén. Improvements in Quantum SDP-Solving with Applications. In 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 132, pp. 99:1-99:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.ICALP.2019.99

Abstract

Following the first paper on quantum algorithms for SDP-solving by Brandão and Svore [Brandão and Svore, 2017] in 2016, rapid developments have been made on quantum optimization algorithms. In this paper we improve and generalize all prior quantum algorithms for SDP-solving and give a simpler and unified framework.
We take a new perspective on quantum SDP-solvers and introduce several new techniques. One of these is the quantum operator input model, which generalizes the different input models used in previous work, and essentially any other reasonable input model. This new model assumes that the input matrices are embedded in a block of a unitary operator. In this model we give a O~((sqrt{m}+sqrt{n}gamma)alpha gamma^4) algorithm, where n is the size of the matrices, m is the number of constraints, gamma is the reciprocal of the scale-invariant relative precision parameter, and alpha is a normalization factor of the input matrices. In particular for the standard sparse-matrix access, the above result gives a quantum algorithm where alpha=s. We also improve on recent results of Brandão et al. [Fernando G. S. L. Brandão et al., 2018], who consider the special case when the input matrices are proportional to mixed quantum states that one can query. For this model Brandão et al. [Fernando G. S. L. Brandão et al., 2018] showed that the dependence on n can be replaced by a polynomial dependence on both the rank and the trace of the input matrices. We remove the dependence on the rank and hence require only a dependence on the trace of the input matrices.
After we obtain these results we apply them to a few different problems. The most notable of which is the problem of shadow tomography, recently introduced by Aaronson [Aaronson, 2018]. Here we simultaneously improve both the sample and computational complexity of the previous best results. Finally we prove a new Omega~(sqrt{m}alpha gamma) lower bound for solving LPs and SDPs in the quantum operator model, which also implies a lower bound for the model of Brandão et al. [Fernando G. S. L. Brandão et al., 2018].

Subject Classification

ACM Subject Classification
  • Theory of computation → Quantum computation theory
Keywords
  • quantum algorithms
  • semidefinite programming
  • shadow tomography

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