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Representations of Boolean functions by real polynomials play an important role in complexity theory. Typically, one is interested in the least degree of a polynomial p(x_1,...,x_n) that approximates or sign-represents a given Boolean…

Computational Complexity · Computer Science 2008-05-15 Alexander A. Sherstov

We prove that for every parity decision tree of depth $d$ on $n$ variables, the sum of absolute values of Fourier coefficients at level $\ell$ is at most $d^{\ell/2} \cdot O(\ell \cdot \log(n))^\ell$. Our result is nearly tight for small…

Computational Complexity · Computer Science 2021-05-14 Uma Girish , Avishay Tal , Kewen Wu

We prove a near optimal round-communication tradeoff for the two-party quantum communication complexity of disjointness. For protocols with $r$ rounds, we prove a lower bound of $\tilde{\Omega}(n/r + r)$ on the communication required for…

Computational Complexity · Computer Science 2015-05-13 Mark Braverman , Ankit Garg , Young Kun Ko , Jieming Mao , Dave Touchette

We study the problem of reaching agreement in a synchronous distributed system by $n$ autonomous parties, when the communication links from/to faulty parties can omit messages. The faulty parties are selected and controlled by an adaptive,…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-05-27 Mohammad T. Hajiaghayi , Dariusz R. Kowalski , Jan Olkowski

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)…

We prove a new lower bound on the parity decision tree complexity $\mathsf{D}_{\oplus}(f)$ of a Boolean function $f$. Namely, granularity of the Boolean function $f$ is the smallest $k$ such that all Fourier coefficients of $f$ are integer…

Computational Complexity · Computer Science 2018-10-29 Anastasiya Chistopolskaya , Vladimir V. Podolskii

In a recent result, Knop, Lovett, McGuire and Yuan (STOC 2021) proved the log-rank conjecture for communication complexity, up to log n factor, for any Boolean function composed with AND function as the inner gadget. One of the main tools…

Computational Complexity · Computer Science 2024-06-14 Farzan Byramji , Vatsal Jha , Chandrima Kayal , Rajat Mittal

For a $\{0,1\}$-valued matrix $M$ let $\rm{CC}(M)$ denote the deterministic communication complexity of the boolean function associated with $M$. The log-rank conjecture of Lov\'{a}sz and Saks [FOCS 1988] states that $\rm{CC}(M) \leq…

Computational Complexity · Computer Science 2011-11-28 Eli Ben-Sasson , Shachar Lovett , Noga Zewi

Let $R_\epsilon(\cdot)$ stand for the bounded-error randomized query complexity with error $\epsilon > 0$. For any relation $f \subseteq \{0,1\}^n \times S$ and partial Boolean function $g \subseteq \{0,1\}^m \times \{0,1\}$, we show that…

Computational Complexity · Computer Science 2018-11-28 Dmitry Gavinsky , Troy Lee , Miklos Santha , Swagato Sanyal

We establish two results regarding the query complexity of bounded-error randomized algorithms. * Bounded-error separation theorem. There exists a total function $f : \{0,1\}^n \to \{0,1\}$ whose $\epsilon$-error randomized query complexity…

Computational Complexity · Computer Science 2019-08-06 Eric Blais , Joshua Brody

This paper explores a fine-grained version of the Watrous conjecture, including the randomized and quantum algorithms with success probabilities arbitrarily close to $1/2$. Our contributions include the following: i) An analysis of the…

Computational Complexity · Computer Science 2023-10-24 Supartha Podder , Penghui Yao , Zekun Ye

We revisit the problem of computing with noisy information considered in Feige et al. 1994, which includes computing the OR function from noisy queries, and computing the MAX, SEARCH and SORT functions from noisy pairwise comparisons. For…

Data Structures and Algorithms · Computer Science 2023-06-22 Banghua Zhu , Ziao Wang , Nadim Ghaddar , Jiantao Jiao , Lele Wang

We show a nearly quadratic separation between deterministic communication complexity and the logarithm of the partition number, which is essentially optimal. This improves upon a recent power 1.5 separation of G\"o\"os, Pitassi, and Watson…

Computational Complexity · Computer Science 2016-05-24 Robin Kothari

The approximate degree of a Boolean function is the minimum degree of real polynomial that approximates it pointwise. For any Boolean function, its approximate degree serves as a lower bound on its quantum query complexity, and generically…

Computational Complexity · Computer Science 2023-05-23 Mark Bun , Nadezhda Voronova

We consider the task of properly PAC learning decision trees with queries. Recent work of Koch, Strassle, and Tan showed that the strictest version of this task, where the hypothesis tree $T$ is required to be optimally small, is NP-hard.…

Computational Complexity · Computer Science 2024-07-02 Caleb Koch , Carmen Strassle , Li-Yang Tan

Sampling from the output distribution of chaotic quantum evolutions, and of pseudo-random universal quantum circuits in particular, has been proposed as a prominent milestone for near-term quantum supremacy. The same paper notes that…

Quantum Physics · Physics 2017-08-08 Sergio Boixo , Vadim N. Smelyanskiy , Hartmut Neven

We define and study the complexity of robust polynomials for Boolean functions and the related fault-tolerant quantum decision trees, where input bits are perturbed by noise. We compare several different possible definitions. Our main…

Quantum Physics · Physics 2007-05-23 Harry Buhrman , Ilan Newman , Hein Roehrig , Ronald de Wolf

We prove a general lower bound on the bounded-error entanglement-assisted quantum communication complexity of Boolean functions. The bound is based on the concept that any classical or quantum protocol to evaluate a function on distributed…

Quantum Physics · Physics 2011-11-09 Ashley Montanaro , Andreas Winter

For any $\{0,1\}$-valued function $f$, its \emph{$n$-folded XOR} is the function $f^{\oplus n}$ where $f^{\oplus n}(X_1, \ldots, X_n) = f(X_1) \oplus \cdots \oplus f(X_n)$. Given a procedure for computing the function $f$, one can apply a…

Computational Complexity · Computer Science 2025-06-17 Pachara Sawettamalya , Huacheng Yu

In the decision tree computation model for Boolean functions, the depth corresponds to query complexity, and size corresponds to storage space. The depth measure is the most well-studied one, and is known to be polynomially related to…

Computational Complexity · Computer Science 2022-09-27 Yogesh Dahiya , Meena Mahajan