Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1911.13091

Hybrid Decision Trees: Longer Quantum Time is Strictly More Powerful

Xiaoming Sun, Yufan Zheng

Published Nov 29, 2019• 2 citations
arXivHTMLPDF

Abstract

In this paper, we introduce the hybrid query complexity, denoted as $\mathrm{Q}(f;q)$, which is the minimal query number needed to compute $f$, when a classical decision tree is allowed to call $q'$-query quantum subroutines for any $q'\leq q$. We present the following results: $\bullet$ There exists a total Boolean function $f$ such that $\mathrm{Q}(f;1) = \widetilde{\mathcal{O}}(\mathrm{R}(f)^{4/5})$. $\bullet$ $\mathrm{Q}(f;q) = Ω(\mathrm{bs}(f)/q + \sqrt{\mathrm{bs}(f)})$ for any Boolean function $f$; the lower bound is tight when $f$ is the ${\rm O{\small R}}$ function. $\bullet$ $\mathrm{Q}(g \circ {\rm X{\small OR}}_{C \log n};1) = \widetildeΩ(\sqrt{n})$ for some sufficiently large constant $C$, where $g := {\rm B{\small OOL}S{\small IMON}}_n$ is a variant of Simon's problem. Note that $\mathrm{Q}(g\circ {\rm X{\small OR}}_{C \log n}) = \mathcal{O}(\mathrm{polylog}\; n)$. Therefore an exponential separation is established. Furthermore, this open the road to prove the conjecture $\forall k,\,\mathrm{Q}(g \circ {\rm X{\small OR}}_{C \log^{k+1} n};\log^{k} n) = \widetildeΩ(\sqrt{n})$, which would imply the oracle separation $\mathsf{HP}(\mathsf{QSIZE}(n^α))^\mathfrak{O} \subsetneq \mathsf{BQP}^\mathfrak{O}$ for any $α$, where $\mathsf{HP}(\mathsf{QSIZE}(n^α))$ is a complexity class that contains $\mathsf{BQTIME}(n^α)^{\mathsf{BPP}}$ and $\mathsf{BPP}^{\mathsf{BQTIME}(n^α)}$ in any relativized world.

DOI
10.48550/arxiv.1911.13091

Related papers

Tight bounds for hybrid quantum-classical query algorithms

Andris Ambainis, András Gilyén, Martins Kokainis

We study hybrid quantum-classical algorithms in the query model. The computation consists of quantum subroutines that make at most $q$ oracle queries; between subroutines, all qubits are measured and discarded. We prove matching upper and lower bounds for several problems in this model: (i) (Unbiased) Phase estimation up to a precision $ε$ requires $Θ(\frac{1}{q ε^2})$ queries; (ii) (Unbiased) Amplitude estimation up to a precision $ε$ requires $Θ(\frac{p(1-p)}{q ε^2})$ queries; (iii) Search among $N$ items requires $Θ(\frac{N}{q})$ queries; (iv) Two level AND-OR tree (AND of $m$ ORs, with $n$ inputs to each OR) requires $Θ(\frac{nm}{q})$ queries. All of these bounds are optimal up to a constant factor, for all $q$ from 1, corresponding to the classical complexity, to $Q(f)$, the unrestricted quantum query complexity of the respective problem. We also derive a hybrid lower bound for distinguishing two distributions specified by a state-preparation unitary; this bound is tight up to a logarithmic factor. Besides specific lower bounds, an important contribution is developing methods for proving lower bounds on hybrid quantum algorithms (which have been very ad-hoc up to now). The first two bounds follow from a common framework: we analyze probability distributions over measurement transcripts and bound a progress measure that captures their distinguishability. The AND-OR bound requires a more delicate argument, combining two progress measures that track the information available to the algorithm classically and in quantum form, respectively.

quant-pharXiv:2610.06803•Oct 6, 2026•0 citations

Classical-Quantum Separations in Minimal Query Complexity of Boolean Functions.

Chandra Sekhar Mukherjee, Subhamoy Maitra

Query complexity is a model of computation in which functions are evaluated by making queries to the variables. In a very recent paper [Physical Review A 101, 022325 (2020)], Chen, Ye and Li provided a characterization of exact one-query quantum algorithms. We first note that this result is an immediate corollary of what was proved five years back by Montanaro, Jozsa and Mitchison [Algorithmica 71, pp. 775-796 (2015)]. Further, in this model, we concentrate on understanding the differences between classical and quantum computation in terms of number of influencing variables of a function given lowest query complexity in the classical domain. We explore the parity decision tree model to portray possible separations between classical and quantum computations in specific cases. Our results classify different classes of Boolean functions on $n$-variables ($n$ being a function of $k$), that can be evaluated (i) with exactly $k$ queries in both classical and exact quantum model or (ii) with $k$ queries in classical model and $(k-1)$ queries in exact quantum model.

•Apr 27, 2020•0 citations

Improved Quantum Query Upper Bounds Based on Classical Decision Trees

Arjan Cornelissen, Nikhil S. Mande, Subhasree Patro

Given a classical query algorithm as a decision tree, when does there exist a quantum query algorithm with a speed-up 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 particular, we obtain a bounded-error quantum query algorithm of cost $O(\sqrt{s})$ to compute a Boolean function (more generally, a relation) that can be computed by a classical (even randomized) decision tree of size $s$. Lin and Lin [ToC'16] and Beigi and Taghavi [Quantum'20] showed results of a similar flavor, and gave upper bounds 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 [Inf. Comp.'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 complexity-theoretic measure. We also show a polynomial separation between rank and randomized rank 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 its edges. 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 asymptotically 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 could yield better upper bounds.

arXiv:2203.02968•Leibniz international proceedings in informatics•Jan 1, 2022•2 citations

Randomized query complexity can beat certificate complexity

Shalev Ben-David, Robin Kothari

A long-standing open question in query complexity asks whether there is a total Boolean function f with R(f) << C(f), where R(f) and C(f) denote its bounded-error randomized query complexity and certificate complexity, respectively. We construct a function with R(f) = O~(sqrt{C(f)}), which is optimal up to log factors. The same function also has $Q(f) = O~(C(f)^{1/4}), where Q(f) is the bounded-error quantum query complexity of f, which is also nearly optimal.

cs.CCCross-listedarXiv:2609.15063•Sep 15, 2026•0 citations

Improved Quantum Query Upper Bounds Based on Classical Decision Trees

Arjan Cornelissen, Nikhil S. Mande, Subhasree Patro

Given a classical query algorithm as a decision tree, when does there exist a quantum query algorithm with a speed-up 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 particular, we obtain 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 .Lin and Lin [ToC'16] and Beigi and Taghavi [Quantum'20] showed results of a similar flavor, and gave upper bounds 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 [Inf. Comp.'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 complexity-theoretic measure. We also show a polynomial separation between rank and randomized rank 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 its edges. 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 asymptotically 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 could yield better upper bounds.

Quantum•Jun 23, 2025•0 citations

THEORETICAL FOUNDATIONS OF CLASSICAL AND QUANTUM QUERY MODELS: COMPLEXITY, LOWER BOUNDS, AND POLYNOMIAL-SPECTRAL INTERPLAY

Medak Srinivas

Query complexity provides a basic framework to quantify the inherent computational cost of functions in the model where access to the input is limited to oracle queries. In this paper, we provide a detailed discussion of classical and quantum models of inquiry, emphasizing the structural differences and the common mathematical basis. We consider deterministic, randomized and quantum query complexities and highlight key techniques to prove lower bounds such as adversarial techniques, polynomial approximations and hybrid arguments . The main theme of this paper is the polynomial-spectral relation which underlies both the classical and the quantum models. In the classical setting, the decision tree complexity is closely related to the degree of real polynomials representing Boolean functions. In the quantum setting, the relation involves the spectral properties of the operators controlling quantum query algorithms, and gives deep insights into quantum accelerations. We study tight bounds for basic problems such as search and element distinctness, proving separations between classical and quantum query complexity. The present study uses methods from approximation theory, linear algebra and spectrum analysis to build a foundation for a complete understanding of the power and limitations of query-based computation.

Iterative International Publishers eBooks•May 1, 2026•0 citations
In this paper, we introduce the hybrid query complexity, denoted as Q(f;q)\mathrm{Q}(f;q)Q(f;q), which is the minimal query number needed to compute fff, when a classical decision tree is allowed to call q′q'q′-query quantum subroutines for any q′≤qq'\leq qq′≤q. We present the following results: ∙\bullet∙ There exists a total Boolean function fff such that Q(f;1)=O~(R(f)4/5)\mathrm{Q}(f;1) = \widetilde{\mathcal{O}}(\mathrm{R}(f)^{4/5})Q(f;1)=O(R(f)4/5). ∙\bullet∙ Q(f;q)=Ω(bs(f)/q+bs(f))\mathrm{Q}(f;q) = Ω(\mathrm{bs}(f)/q + \sqrt{\mathrm{bs}(f)})Q(f;q)=Ω(bs(f)/q+bs(f)​) for any Boolean function fff; the lower bound is tight when fff is the OR{\rm O{\small R}}OR function. ∙\bullet∙ Q(g∘XORClog⁡n;1)=Ω~(n)\mathrm{Q}(g \circ {\rm X{\small OR}}_{C \log n};1) = \widetildeΩ(\sqrt{n})Q(g∘XORClogn​;1)=Ω(n​) for some sufficiently large constant CCC, where g:=BOOLSIMONng := {\rm B{\small OOL}S{\small IMON}}_ng:=BOOLSIMONn​ is a variant of Simon's problem. Note that Q(g∘XORClog⁡n)=O(polylog  n)\mathrm{Q}(g\circ {\rm X{\small OR}}_{C \log n}) = \mathcal{O}(\mathrm{polylog}\; n)Q(g∘XORClogn​)=O(polylogn). Therefore an exponential separation is established. Furthermore, this open the road to prove the conjecture ∀k, Q(g∘XORClog⁡k+1n;log⁡kn)=Ω~(n)\forall k,\,\mathrm{Q}(g \circ {\rm X{\small OR}}_{C \log^{k+1} n};\log^{k} n) = \widetildeΩ(\sqrt{n})∀k,Q(g∘XORClogk+1n​;logkn)=Ω(n​), which would imply the oracle separation HP(QSIZE(nα))O⊊BQPO\mathsf{HP}(\mathsf{QSIZE}(n^α))^\mathfrak{O} \subsetneq \mathsf{BQP}^\mathfrak{O}HP(QSIZE(nα))O⊊BQPO for any ααα, where HP(QSIZE(nα))\mathsf{HP}(\mathsf{QSIZE}(n^α))HP(QSIZE(nα)) is a complexity class that contains BQTIME(nα)BPP\mathsf{BQTIME}(n^α)^{\mathsf{BPP}}BQTIME(nα)BPP and BPPBQTIME(nα)\mathsf{BPP}^{\mathsf{BQTIME}(n^α)}BPPBQTIME(nα) in any relativized world.
We study hybrid quantum-classical algorithms in the query model. The computation consists of quantum subroutines that make at most qqq oracle queries; between subroutines, all qubits are measured and discarded. We prove matching upper and lower bounds for several problems in this model: (i) (Unbiased) Phase estimation up to a precision εεε requires Θ(1qε2)Θ(\frac{1}{q ε^2})Θ(qε21​) queries; (ii) (Unbiased) Amplitude estimation up to a precision εεε requires Θ(p(1−p)qε2)Θ(\frac{p(1-p)}{q ε^2})Θ(qε2p(1−p)​) queries; (iii) Search among NNN items requires Θ(Nq)Θ(\frac{N}{q})Θ(qN​) queries; (iv) Two level AND-OR tree (AND of mmm ORs, with nnn inputs to each OR) requires Θ(nmq)Θ(\frac{nm}{q})Θ(qnm​) queries. All of these bounds are optimal up to a constant factor, for all qqq from 1, corresponding to the classical complexity, to Q(f)Q(f)Q(f), the unrestricted quantum query complexity of the respective problem. We also derive a hybrid lower bound for distinguishing two distributions specified by a state-preparation unitary; this bound is tight up to a logarithmic factor. Besides specific lower bounds, an important contribution is developing methods for proving lower bounds on hybrid quantum algorithms (which have been very ad-hoc up to now). The first two bounds follow from a common framework: we analyze probability distributions over measurement transcripts and bound a progress measure that captures their distinguishability. The AND-OR bound requires a more delicate argument, combining two progress measures that track the information available to the algorithm classically and in quantum form, respectively.
Query complexity is a model of computation in which functions are evaluated by making queries to the variables. In a very recent paper [Physical Review A 101, 022325 (2020)], Chen, Ye and Li provided a characterization of exact one-query quantum algorithms. We first note that this result is an immediate corollary of what was proved five years back by Montanaro, Jozsa and Mitchison [Algorithmica 71, pp. 775-796 (2015)]. Further, in this model, we concentrate on understanding the differences between classical and quantum computation in terms of number of influencing variables of a function given lowest query complexity in the classical domain. We explore the parity decision tree model to portray possible separations between classical and quantum computations in specific cases. Our results classify different classes of Boolean functions on nnn-variables (nnn being a function of kkk), that can be evaluated (i) with exactly kkk queries in both classical and exact quantum model or (ii) with kkk queries in classical model and (k−1)(k-1)(k−1) queries in exact quantum model.
Given a classical query algorithm as a decision tree, when does there exist a quantum query algorithm with a speed-up 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 particular, we obtain a bounded-error quantum query algorithm of cost O(s)O(\sqrt{s})O(s​) to compute a Boolean function (more generally, a relation) that can be computed by a classical (even randomized) decision tree of size sss. Lin and Lin [ToC'16] and Beigi and Taghavi [Quantum'20] showed results of a similar flavor, and gave upper bounds 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 [Inf. Comp.'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 complexity-theoretic measure. We also show a polynomial separation between rank and randomized rank 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 its edges. 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 asymptotically 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 could yield better upper bounds.
Given a classical query algorithm as a decision tree, when does there exist a quantum query algorithm with a speed-up 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 particular, we obtain 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 .Lin and Lin [ToC'16] and Beigi and Taghavi [Quantum'20] showed results of a similar flavor, and gave upper bounds 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 [Inf. Comp.'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 complexity-theoretic measure. We also show a polynomial separation between rank and randomized rank 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 its edges. 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 asymptotically 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 could yield better upper bounds.
Query complexity provides a basic framework to quantify the inherent computational cost of functions in the model where access to the input is limited to oracle queries. In this paper, we provide a detailed discussion of classical and quantum models of inquiry, emphasizing the structural differences and the common mathematical basis. We consider deterministic, randomized and quantum query complexities and highlight key techniques to prove lower bounds such as adversarial techniques, polynomial approximations and hybrid arguments . The main theme of this paper is the polynomial-spectral relation which underlies both the classical and the quantum models. In the classical setting, the decision tree complexity is closely related to the degree of real polynomials representing Boolean functions. In the quantum setting, the relation involves the spectral properties of the operators controlling quantum query algorithms, and gives deep insights into quantum accelerations. We study tight bounds for basic problems such as search and element distinctness, proving separations between classical and quantum query complexity. The present study uses methods from approximation theory, linear algebra and spectrum analysis to build a foundation for a complete understanding of the power and limitations of query-based computation.