Improved Quantum Query Upper Bounds Based on Classical Decision Trees

Authors Arjan Cornelissen, Nikhil S. Mande, Subhasree Patro



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Author Details

Arjan Cornelissen
  • Institute for Logic, Language, and Computation, University of Amsterdam, The Netherlands
  • QuSoft, Amsterdam, The Netherlands
Nikhil S. Mande
  • QuSoft, Amsterdam, The Netherlands
  • CWI, Amsterdam, The Netherlands
Subhasree Patro
  • QuSoft, Amsterdam, The Netherlands
  • CWI Amsterdam, The Netherlands
  • University of Amsterdam, The Netherlands

Acknowledgements

We thank Ronald de Wolf for useful comments.

Cite AsGet BibTex

Arjan Cornelissen, Nikhil S. Mande, and Subhasree Patro. Improved Quantum Query Upper Bounds Based on Classical Decision Trees. In 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 250, pp. 15:1-15:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.FSTTCS.2022.15

Abstract

We consider the following question in query complexity: Given a classical query algorithm in the form of a decision tree, when does there exist a quantum query algorithm with a speed-up (i.e., that makes fewer queries) over the classical one? We provide a general construction based on the structure of the underlying decision tree, and prove that this can give us an up-to-quadratic quantum speed-up in the number of queries. In particular, our results give a bounded-error quantum query algorithm of cost O(√s) to compute a Boolean function (more generally, a relation) that can be computed by a classical (even randomized) decision tree of size s. This recovers an O(√n) algorithm for the Search problem, for example. Lin and Lin [Theory of Computing'16] and Beigi and Taghavi [Quantum'20] showed results of a similar flavor. Their upper bounds are in terms of a quantity which we call the "guessing complexity" of a decision tree. We identify that the guessing complexity of a decision tree equals its rank, a notion introduced by Ehrenfeucht and Haussler [Information and Computation'89] in the context of learning theory. This answers a question posed by Lin and Lin, who asked whether the guessing complexity of a decision tree is related to any measure studied in classical complexity theory. We also show a polynomial separation between rank and its natural randomized analog for the complete binary AND-OR tree. Beigi and Taghavi constructed span programs and dual adversary solutions for Boolean functions given classical decision trees computing them and an assignment of non-negative weights to edges of the tree. We explore the effect of changing these weights on the resulting span program complexity and objective value of the dual adversary bound, and capture the best possible weighting scheme by an optimization program. We exhibit a solution to this program and argue its optimality from first principles. We also exhibit decision trees for which our bounds are strictly stronger than those of Lin and Lin, and Beigi and Taghavi. This answers a question of Beigi and Taghavi, who asked whether different weighting schemes in their construction could yield better upper bounds.

Subject Classification

ACM Subject Classification
  • Theory of computation → Oracles and decision trees
  • Theory of computation → Quantum complexity theory
Keywords
  • Quantum Query Complexity
  • Decision Trees
  • Decision Tree Rank

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