For any function $f: X \times Y \to Z$, we prove that $Q^{*\text{cc}}(f) \cdot Q^{\text{OIP}}(f) \cdot (\log Q^{\text{OIP}}(f) + \log |Z|) \geq Ω(\log |X|)$. Here, $Q^{*\text{cc}}(f)$ denotes the bounded-error communication complexity of $f$ using an entanglement-assisted two-way qubit channel, and $Q^{\text{OIP}}(f)$ denotes the number of quantum queries needed to learn $x$ with high probability given oracle access to the function $f_x(y) \stackrel{\text{def}}{=} f(x, y)$. We show that this tradeoff is close to the best possible. We also give a generalization of this tradeoff for distributional query complexity. As an application, we prove an optimal $Ω(\log q)$ lower bound on the $Q^{*\text{cc}}$ complexity of determining whether $x + y$ is a perfect square, where Alice holds $x \in \mathbf{F}_q$, Bob holds $y \in \mathbf{F}_q$, and $\mathbf{F}_q$ is a finite field of odd characteristic. As another application, we give a new, simpler proof that searching an ordered size-$N$ database requires $Ω(\log N / \log \log N)$ quantum queries. (It was already known that $Θ(\log N)$ queries are required.)
For any function f:X×Y→Z, we prove that Q∗cc(f)⋅QOIP(f)⋅(logQOIP(f)+log∣Z∣)≥Ω(log∣X∣). Here, Q∗cc(f) denotes the bounded-error communication complexity of f using an entanglement-assisted two-way qubit channel, and QOIP(f) denotes the number of quantum queries needed to learn x with high probability given oracle access to the function fx(y)=deff(x,y). We show that this tradeoff is close to the best possible. We also give a generalization of this tradeoff for distributional query complexity. As an application, we prove an optimal Ω(logq) lower bound on the Q∗cc complexity of determining whether x+y is a perfect square, where Alice holds x∈Fq, Bob holds y∈Fq, and Fq is a finite field of odd characteristic. As another application, we give a new, simpler proof that searching an ordered size-N database requires Ω(logN/loglogN) quantum queries. (It was already known that Θ(logN) queries are required.)
Buhrman, Cleve and Wigderson (STOC'98) showed that for every Boolean function f : {-1,1}ⁿ → {-1,1} and G ∈ {AND₂, XOR₂}, the bounded-error quantum communication complexity of the composed function f∘G equals O(𝖰(f) log n), where 𝖰(f) denotes the bounded-error quantum query complexity of f. This is achieved by Alice running the optimal quantum query algorithm for f, using a round of O(log n) qubits of communication to implement each query. This is in contrast with the classical setting, where it is easy to show that 𝖱^{cc}(f∘G) ≤ 2𝖱(f), where 𝖱^{cc} and 𝖱 denote bounded-error communication and query complexity, respectively. Chakraborty et al. (CCC'20) exhibited a total function for which the log n overhead in the BCW simulation is required. This established the somewhat surprising fact that quantum reductions are in some cases inherently more expensive than classical reductions. We improve upon their result in several ways. - We show that the log n overhead is not required when f is symmetric (i.e., depends only on the Hamming weight of its input), generalizing a result of Aaronson and Ambainis for the Set-Disjointness function (Theory of Computing'05). Our upper bound assumes a shared entangled state, though for most symmetric functions the assumed number of entangled qubits is less than the communication and hence could be part of the communication. - In order to prove the above, we design an efficient distributed version of noisy amplitude amplification that allows us to prove the result when f is the OR function. This also provides a different, and arguably simpler, proof of Aaronson and Ambainis’s O(√n) communication upper bound for Set-Disjointness. - In view of our first result above, one may ask whether the log n overhead in the BCW simulation can be avoided even when f is transitive, which is a weaker notion of symmetry. We give a strong negative answer by showing that the log n overhead is still necessary for some transitive functions even when we allow the quantum communication protocol an error probability that can be arbitrarily close to 1/2 (this corresponds to the unbounded-error model of communication). - We also give, among other things, a general recipe to construct functions for which the log n overhead is required in the BCW simulation in the bounded-error communication model, even if the parties are allowed to share an arbitrary prior entangled state for free.
Buhrman, Cleve and Wigderson (STOC'98) showed that for every Boolean function f : {-1,1}^n to {-1,1} and G in {AND_2, XOR_2}, the bounded-error quantum communication complexity of the composed function f o G equals O(Q(f) log n), where Q(f) denotes the bounded-error quantum query complexity of f. This is achieved by Alice running the optimal quantum query algorithm for f, using a round of O(log n) qubits of communication to implement each query. This is in contrast with the classical setting, where it is easy to show that R^{cc}(f o G) is at most 2R(f), where R^{cc} and R denote bounded-error communication and query complexity, respectively. We show that the O(log n) overhead is required for some functions in the quantum setting, and thus the BCW simulation is tight. We note here that prior to our work, the possibility of Q^{cc}(f o G) = O(Q(f)), for all f and all G in {AND_2, XOR_2}, had not been ruled out. More specifically, we show the following. - We show that the log n overhead is *not* required when f is symmetric, generalizing a result of Aaronson and Ambainis for the Set-Disjointness function (Theory of Computing'05). - In order to prove the above, we design an efficient distributed version of noisy amplitude amplification that allows us to prove the result when f is the OR function. - In view of our first result above, one may ask whether the log n overhead in the BCW simulation can be avoided even when f is transitive, which is a weaker notion of symmetry. We give a strong negative answer by showing that the log n overhead is still necessary for some transitive functions even when we allow the quantum communication protocol an error probability that can be arbitrarily close to 1/2. - We also give, among other things, a general recipe to construct functions for which the log n overhead is required in the BCW simulation in the bounded-error communication model.
Buhrman, Cleve and Wigderson (STOC'98) observed that for every Boolean function f:{-1,1}ⁿ → {-1,1} and •:{-1,1}² → {-1,1} the two-party bounded-error quantum communication complexity of (f ∘ •) is O(Q(f) log n), where Q(f) is the bounded-error quantum query complexity of f. Note that the bounded-error randomized communication complexity of (f ∘ •) is bounded by O(R(f)), where R(f) denotes the bounded-error randomized query complexity of f. Thus, the BCW simulation has an extra O(log n) factor appearing that is absent in classical simulation. A natural question is if this factor can be avoided. Razborov (IZV MATH'03) showed that the bounded-error quantum communication complexity of Set-Disjointness is Ω(√n). The BCW simulation yields an upper bound of O(√n log n). Høyer and de Wolf (STACS'02) showed that this can be reduced to c^(log^* n) for some constant c, and subsequently Aaronson and Ambainis (FOCS'03) showed that this factor can be made a constant. That is, the quantum communication complexity of the Set-Disjointness function (which is NOR_n ∘ ∧) is O(Q(NOR_n)). Perhaps somewhat surprisingly, we show that when • = ⊕, then the extra log n factor in the BCW simulation is unavoidable. In other words, we exhibit a total function F:{-1,1}ⁿ → {-1,1} such that Q^{cc}(F ∘ ⊕) = Θ(Q(F) log n). To the best of our knowledge, it was not even known prior to this work whether there existed a total function F and 2-bit function •, such that Q^{cc}(F ∘ •) = ω(Q(F)).
In this paper, we focus on the quantum communication complexity of functions of the form f∘G=f(G(X1,Y1),…,G(Xn,Yn)) where $f: \Bset^n \to \Bset$ is a symmetric function, $G: \Bset^j \times \Bset^k \to \Bset$ is any function and Alice (resp. Bob) is given (Xi)i∈[n] (resp. (Yi)i∈[n]). Recently, Chakraborty et al. [STACS 2022] showed that the quantum communication complexity of f∘G is $O(Q(f)\QCCEX(G))$ when the parties are allowed to use shared entanglement, where Q(f) is the query complexity of f and $\QCCEX(G)$ is the exact communication complexity of G. }{In this paper, we first show that the same statement holds \emph{without both shared entanglement and shared randomness}, which generalizes their result. Based on the improved result, we next show tight upper bounds on f∘AND2 for any symmetric function f (where $\textrm{AND}_2 : \Bset \times \Bset \to \Bset$ denotes the 2-bit AND function) in both models: with shared entanglement and without shared entanglement. This matches the well-known lower bound by Razborov~[Izv. Math. 67(1) 145, 2003] when shared entanglement is allowed and improves Razborov's bound when shared entanglement is not allowed.
We fully determine the communication complexity of approximating matrix rank, over any finite field F. We study the most general version of this problem, where0⩽r<R⩽nare given integers and Alice and Bob need to determine whether their respective matricesA,B∈Fn×nsatisfy rk(A+B)=rversus rk(A+B)=R. We show that this problem has commu-nication costΩ(r2log∣F∣), which is optimal. Our lower bound holds even for quantum protocols and even for error probability21(1−∣F∣−r/3), which too is optimal because this problem has a two-bit classical protocol with error21(1−∣F∣−Θ(r)). Prior to our work, lower bounds were known only for constant-error protocols and only for consecutive integersrandR, with no implication for the approximation of matrix rank. We also settle an analogous question for subspaces, where Alice has a subspaceS, Bob has a subspaceT) and they need to approximate the dimension of the subspaceS+Tgenerated bySandT(equivalently, approximate the dimension ofS∩T). As an application, we obtain anΩ(n2log∣F∣)/kmemory lower bound for any streaming algorithm withkpasses that approximates the rank of an input matrixM∈Fn×nwithin a factor of2−δ, for anyδ>0. Our result is an exponential improvement inkover previous work.
Quantum linear system solvers form one of the central algorithmic primitives in quantum computing, with applications ranging from differential equations and optimization to machine learning. Their cost is commonly measured through query complexity, which counts the number of oracle calls needed to access the input matrix. In the sparse-access model, this complexity is governed by three parameters: the condition number κ, the sparsity s of the input matrix, and the target precision ε. The dependence on κ and ε is already well understood through the lower bound Ω(κlog(1/ε)), which matches the best known scaling in these parameters. Once the sparsity s is included, however, the expected lower bound has long been conjectured to be Ω(κslog(1/ε)). Recent work by Mori et al. [Quantum Sci. Tech. 11 035063 (2026)] made an important step towards this goal by establishing the lower bound Ω(κs) for constant error ε. In this work, we complete the picture and prove the full joint lower bound Ω(κslog(1/ε)) in the sparse-access model, thereby establishing the anticipated dependence on all three parameters simultaneously.