We prove that the randomized communication complexity of Vector-in-Subspace in $\mathbb R^n$ with error at most $1/3$ is $Θ(\sqrt n)$. Alice holds a unit vector $u$ and Bob holds an $n/2$-dimensional subspace $H$; they must distinguish $u\in H$ from $u\in H^\perp$. An exact one-way quantum protocol uses $O(\log n)$ qubits, making the problem a geometric example of an exponential quantum advantage over interactive classical communication. Our lower bound matches Raz's $O(\sqrt n)$ upper bound, improves the $Ω(n^{1/3})$ lower bound of Klartag and Regev, and removes the total-rank restriction in Grupel's $Ω(\sqrt n)$ result.
The proof establishes a Gaussian moment bound over complete rectangle partitions. A complex Gaussian representation yields a vanishing even moment whose cancellation is preserved when averaging over the partition. We control the signed conditional moments around a real center by decomposing conditional Gaussian measures into fixed coordinates and sub-Gaussian components. Combining this estimate with spherical harmonic analysis gives the communication lower bound.
We prove that the randomized communication complexity of Vector-in-Subspace in Rn with error at most 1/3 is Θ(n). Alice holds a unit vector u and Bob holds an n/2-dimensional subspace H; they must distinguish u∈H from u∈H⊥. An exact one-way quantum protocol uses O(logn) qubits, making the problem a geometric example of an exponential quantum advantage over interactive classical communication. Our lower bound matches Raz's O(n) upper bound, improves the Ω(n1/3) lower bound of Klartag and Regev, and removes the total-rank restriction in Grupel's Ω(n) result.
The proof establishes a Gaussian moment bound over complete rectangle partitions. A complex Gaussian representation yields a vanishing even moment whose cancellation is preserved when averaging over the partition. We control the signed conditional moments around a real center by decomposing conditional Gaussian measures into fixed coordinates and sub-Gaussian components. Combining this estimate with spherical harmonic analysis gives the communication lower bound.
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.
We study an extension of the standard two-party communication model in which Alice and Bob hold probability distributions p and q over domains X and Y, respectively. Their goal is to estimate Ex∼p,y∼q[f(x,y)] to within additive error ε for a bounded function f, known to both parties. We refer to this as the distributed estimation problem. Special cases of this problem arise in a variety of areas including sketching, databases and learning. Our goal is to understand how the required communication scales with the communication complexity of f and the error parameter ε. The random sampling approach -- estimating the mean by averaging f over O(1/ε2) random samples -- requires O(R(f)/ε2) total communication, where R(f) is the randomized communication complexity of f. We design a new debiasing protocol which improves the dependence on 1/ε to be linear instead of quadratic. Additionally we show better upper bounds for several special classes of functions, including the Equality and Greater-than functions. We introduce lower bound techniques based on spectral methods and discrepancy, and show the optimality of many of our protocols: the debiasing protocol is tight for general functions, and that our protocols for the equality and greater-than functions are also optimal. Furthermore, we show that among full-rank Boolean functions, Equality is essentially the easiest.
We characterize the communication complexity of the following distributed estimation problem. Alice and Bob observe infinitely many iid copies of ρ-correlated unit-variance (Gaussian or ±1 binary) random variables, with unknown ρ∈[−1,1]. By interactively exchanging k bits, Bob wants to produce an estimate ρ of ρ. We show that the best possible performance (optimized over interaction protocol Π and estimator ρ) satisfies infΠ ρsupρE [|ρ−ρ|2] = k−1 (1/2 ln2 + o(1)). Curiously, the number of samples in our achievability scheme is exponential in k; by contrast, a naive scheme exchanging k samples achieves the same Ω(1/k) rate but with a suboptimal prefactor. Our protocol achieving optimal performance is one-way (non-interactive). We also prove the Ω(1/k) bound even when ρ is restricted to any small open sub-interval of [−1,1] (i.e. a local minimax lower bound). Our proof techniques rely on symmetric strong data-processing inequalities and various tensorization techniques from information-theoretic interactive common-randomness extraction. Our results also imply an Ω(n) lower bound on the information complexity of the Gap-Hamming problem, for which we show a direct information-theoretic proof.
Alice and Bob hold complementary halves of the columns of an n×n matrix of k-bit integers and decide whether it is singular over Q. Up to constant-factor dimension growth, this partitioned problem is equivalent to additive singularity, where Alice and Bob hold n×n matrices A,B of k-bit integers and test the singularity of A+B over Q. We prove a bounded-error quantum lower bound Ω(min{n3/2klogn,n2log(nk)}) and a randomized upper bound O(n2min{k,log(k+logn)}). The bounds match at Θ(n2log(nk)) for k≥nlogn. The lower bound holds even under the promise of rank n−1 versus n. Its proof combines an integer witness with a local digit encoding, while the upper-bound protocol lifts modular kernel relations using short messages.
Bitwise sharing changes how the communication complexity depends on the entry bit length k. If Alice and Bob instead hold n×n matrices A,B of k-bit integers and test whether their entrywise bitwise XOR A⊕B is singular over Q, we prove a quantum lower bound Ω(n2k), matching the trivial upper bound. This lower bound holds even under the promise of rank n−1 versus n. Our proof uses a dense weighted equation and a signed Fourier witness whose exceptional dyadic frequencies can be removed at small variation cost.
We study the one-way communication complexity of the f-Boolean Hidden Partition problem. Here, Alice is given an n-bit string and Bob is given Ω(n) disjoint blocks of its indices together with a string of labels. Under the promise that the evaluations of f on these blocks either agree with all of the labels or disagree with all of them, Bob must determine which is the case using a single message from Alice. This problem generalizes the Boolean Hidden Matching and Hidden Hypermatching problems, which capture the special case where f is the parity function.
We establish classical and quantum communication bounds for f-Boolean Hidden Partition in terms of the sign degree d of f, proving a conjecture of Doriguello and Montanaro (TQC 2020). Their prior work gave logarithmic communication upper bounds for classical protocols when d≤1 and quantum protocols when d≤2, as well as polynomial lower bounds for certain structured functions with larger sign degree. We show that for every f of sign degree d≥2, the randomized classical communication complexity of this problem is Θ(n1−1/d) while its quantum communication complexity lies between Ω(n1−2/d) and $\bO{n^{1-1/\lceil d/2 \rceil} \log n}$. This completely characterizes the functions f admitting polynomial quantum advantage as those for which d≥2, with a new infinite family of exponential separations given by the case d=2.
We study the question of how much classical communication is needed when Alice is given a classical description of a quantum state ∣ψ⟩ for Bob to recover any expectation value ⟨ψ∣M∣ψ⟩ given an observable M with M Hermitian and ∣∣M∣∣op≤1. This task, whose study was initiated by Raz (ACM 1999) and more recently investigated by Gosset and Smolin (TQC 2019), can be thought of as a fully classical version of the pure state case of the well-known classical shadows problem in quantum learning theory. We show how the hardness of these two seemingly distinct problems are connected. We first consider the relative error version of the communication question and prove a lower bound of Ω(2nε−2) on the one-way randomized classical communication, improving upon an additive error lower bound of Ω(2n) as shown by Gosset and Smolin. Notably, we show that this lower bound holds not only for the set of all observables but also when restricted to just the class of Pauli observables. This fact implies a Ω(2n) versus O(poly(n)) separation in the compression size between the relative and additive error settings for non-adaptive Pauli classical shadows with classical memory. Extending this framework, we prove randomized communication lower bounds for other relative error one-way classical communication tasks: an Ω(2nε−2) lower bound when instead Alice is given an observable and Bob is given a quantum state and they are asked to estimate the expectation value, an Ω(nε−2) lower bound when restricted to Paulis, and an Ω(2nε−2) lower bound when Alice and Bob are both given quantum states and asked to estimate the inner product.