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REPORTS > KEYWORD > QUANTUM QUERY COMPLEXITY:
Reports tagged with quantum query complexity:
TR05-041 | 12th April 2005
Shengyu Zhang

(Almost) tight bounds for randomized and quantum Local Search on hypercubes and grids

Revisions: 2

The Local Search problem, which finds a
local minimum of a black-box function on a given graph, is of both
practical and theoretical importance to many areas in computer
science and natural sciences. In this paper, we show that for the
Boolean hypercube $\B^n$, the randomized query complexity of Local
more >>>


TR11-040 | 22nd March 2011
Alexander A. Sherstov

Strong Direct Product Theorems for Quantum Communication and Query Complexity

A strong direct product theorem (SDPT) states that solving $n$ instances of a problem requires $\Omega(n)$ times the resources for a single instance, even to achieve success probability $2^{-\Omega(n)}.$ We prove that quantum communication complexity obeys an SDPT whenever the communication lower bound for a single instance is proved by ... more >>>


TR17-169 | 24th October 2017
Mark Bun, Robin Kothari, Justin Thaler

The Polynomial Method Strikes Back: Tight Quantum Query Bounds via Dual Polynomials

The approximate degree of a Boolean function $f$ is the least degree of a real polynomial that approximates $f$ pointwise to error at most $1/3$. The approximate degree of $f$ is known to be a lower bound on the quantum query complexity of $f$ (Beals et al., FOCS 1998 and ... more >>>


TR18-010 | 14th January 2018
Alexander A. Sherstov

Algorithmic polynomials

The approximate degree of a Boolean function $f(x_{1},x_{2},\ldots,x_{n})$ is the minimum degree of a real polynomial that approximates $f$ pointwise within $1/3$. Upper bounds on approximate degree have a variety of applications in learning theory, differential privacy, and algorithm design in general. Nearly all known upper bounds on approximate degree ... more >>>


TR18-156 | 8th September 2018
Mark Bun, Robin Kothari, Justin Thaler

Quantum algorithms and approximating polynomials for composed functions with shared inputs

We give new quantum algorithms for evaluating composed functions whose inputs may be shared between bottom-level gates. Let $f$ be a Boolean function and consider a function $F$ obtained by applying $f$ to conjunctions of possibly overlapping subsets of $n$ variables. If $f$ has quantum query complexity $Q(f)$, we give ... more >>>


TR19-015 | 7th February 2019
William Kretschmer

QMA Lower Bounds for Approximate Counting

We prove a query complexity lower bound for $QMA$ protocols that solve approximate counting: estimating the size of a set given a membership oracle. This gives rise to an oracle $A$ such that $SBP^A \not\subset QMA^A$, resolving an open problem of Aaronson [2]. Our proof uses the polynomial method to ... more >>>


TR19-136 | 23rd September 2019
Sourav Chakraborty, Arkadev Chattopadhyay, Nikhil Mande, Manaswi Paraashar

Quantum Query-to-Communication Simulation Needs a Logarithmic Overhead

Buhrman, Cleve and Wigderson (STOC'98) observed that for every Boolean function $f : \{-1, 1\}^n \to \{-1, 1\}$ and $\bullet : \{-1, 1\}^2 \to \{-1, 1\}$ the two-party bounded-error quantum communication complexity of $(f \circ \bullet)$ is $O(Q(f) \log n)$, where $Q(f)$ is the bounded-error quantum query complexity of $f$. ... more >>>


TR19-179 | 7th December 2019
Avishay Tal

Towards Optimal Separations between Quantum and Randomized Query Complexities

Revisions: 1

The query model offers a concrete setting where quantum algorithms are provably superior to randomized algorithms. Beautiful results by Bernstein-Vazirani, Simon, Aaronson, and others presented partial Boolean functions that can be computed by quantum algorithms making much fewer queries compared to their randomized analogs. To date, separations of $O(1)$ vs. ... more >>>


TR20-020 | 21st February 2020
Nikhil Mande, Justin Thaler, Shuchen Zhu

Improved Approximate Degree Bounds For $k$-distinctness

An open problem that is widely regarded as one of the most important in quantum query complexity is to resolve the quantum query complexity of the $k$-distinctness function on inputs of size $N$. While the case of $k=2$ (also called Element Distinctness) is well-understood, there is a polynomial gap between ... more >>>




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