Quantum lower bound for recursive Fourier sampling

2003 ◽  
Vol 3 (2) ◽  
pp. 165-174
Author(s):  
S. Aaronson
Keyword(s):  
Lower Bound ◽  
Quantum Algorithm ◽  
Independent Interest ◽  
Classical Algorithm ◽  
Fourier Sampling ◽  
Adversary Method

We revisit the oft-neglected `recursive Fourier sampling' (RFS) problem, introduced by Bernstein and Vazirani to prove an oracle separation between BPP and BQP. We show that the known quantum algorithm for RFS is essentially optimal, despite its seemingly wasteful need to uncompute information. This implies that, to place \mathsf{BQP} outside of PH[\log] relative to an oracle, one would need to go outside the RFS framework. Our proof argues that, given any variant of RFS, either the adversary method of Ambainis yields a good quantum lower bound, or else there is an efficient classical algorithm. This technique may be of independent interest.

scholarly journals Learning DNFs under product distributions via mu--biased quantum Fourier sampling

2019 ◽  
Vol 19 (15&16) ◽  
pp. 1261-1278
Author(s):  
Varun Kanade ◽  
Andrea Rocchetto ◽  
Simone Severini
Keyword(s):  
Fourier Transform ◽  
Uniform Distribution ◽  
Polynomial Time ◽  
Quantum Algorithm ◽  
Classical Algorithm ◽  
Product Distributions ◽  
Fourier Sampling

We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle. The current best classical algorithm runs in superpolynomial time. Our result extends the work by Bshouty and Jackson (1998) that proved that DNF formulae are efficiently learnable under the uniform distribution using a quantum example oracle. Our proof is based on a new quantum algorithm that efficiently samples the coefficients of a μ–biased Fourier transform.

scholarly journals The quantum query complexity of AC0

2012 ◽  
Vol 12 (7&8) ◽  
pp. 670-676
Author(s):  
Paul Beame ◽  
Widad Machmouchi
Keyword(s):  
Lower Bound ◽  
Quantum Algorithm ◽  
Input Function ◽  
Query Complexity ◽  
Quantum Query Complexity ◽  
Quantum Query

We show that any quantum algorithm deciding whether an input function $f$ from $[n]$ to $[n]$ is 2-to-1 or almost 2-to-1 requires $\Theta(n)$ queries to $f$. The same lower bound holds for determining whether or not a function $f$ from $[2n-2]$ to $[n]$ is surjective. These results yield a nearly linear $\Omega(n/\log n)$ lower bound on the quantum query complexity of $\cl{AC}^0$. The best previous lower bound known for any $\cl{AC^0}$ function was the $\Omega ((n/\log n)^{2/3})$ bound given by Aaronson and Shi's $\Omega(n^{2/3})$ lower bound for the element distinctness problem.

scholarly journals A panoply of quantum algorithms

2008 ◽  
Vol 8 (8&9) ◽  
pp. 834-859
Author(s):  
B. Furrow
Keyword(s):  
Quantum Physics ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Breadth First Search ◽  
Basic Tool ◽  
Grover’S Algorithm ◽  
Classical Algorithm ◽  
Grover's Algorithm ◽  
Programming Techniques ◽  
New Algorithms

This paper's aim is to explore improvements to, and applications of, a fundamental quantum algorithm invented by Grover\cite{grover}. Grover's algorithm is a basic tool that can be applied to a large number of problems in computer science, creating quantum algorithms that are polynomially faster than fastest known and fastest possible classical algorithms that solve the same problems. Our goal in this paper is to make these techniques readily accessible to those without a strong background in quantum physics: we achieve this by providing a set of tools, each of which makes use of Grover's algorithm or similar techniques, which can be used as subroutines in many quantum algorithms.}{The tools we provide are carefully constructed: they are easy to use, and in many cases they are asymptotically faster than the best tools previously available. The tools we build on include algorithms by Boyer, Brassard, Hoyer and Tapp, Buhrman, Cleve, de Witt and Zalka and Durr and Hoyer.}{After creating our tools, we create several new quantum algorithms, each of which is faster than the fastest known deterministic classical algorithm that accomplishes the same aim, and some of which are faster than the fastest possible deterministic classical algorithm. These algorithms solve problems from the fields of graph theory and computational geometry, and some employ dynamic programming techniques. We discuss a breadth-first search that is faster than $\Theta(\text{edges})$ (the classical limit) in a dense graph, maximum-points-on-a-line in $O(N^{3/2}\lg N)$ (faster than the fastest classical algorithm known), as well as several other algorithms that are similarly illustrative of solutions in some class of problem. Through these new algorithms we illustrate the use of our tools, working to encourage their use and the study of quantum algorithms in general.

scholarly journals Quantum algorithms for search with wildcards and combinatorial group testing

2014 ◽  
Vol 14 (5&6) ◽  
pp. 439-453
Author(s):  
Andris Ambainis ◽  
Ashley Montanaro
Keyword(s):  
Lower Bound ◽  
Quantum Algorithms ◽  
Group Testing ◽  
Quantum Algorithm ◽  
Quantum Walk ◽  
Combinatorial Problems ◽  
Special Items ◽  
State Discrimination ◽  
Combinatorial Group Testing ◽  
Combinatorial Group

We consider two combinatorial problems. The first we call ``search with wildcards'': given an unknown $n$-bit string $x$, and the ability to check whether any subset of the bits of $x$ is equal to a provided query string, the goal is to output $x$. We give a nearly optimal $O(\sqrt{n} \log n)$ quantum query algorithm for search with wildcards, beating the classical lower bound of $\Omega(n)$ queries. Rather than using amplitude amplification or a quantum walk, our algorithm is ultimately based on the solution to a state discrimination problem. The second problem we consider is combinatorial group testing, which is the task of identifying a subset of at most $k$ special items out of a set of $n$ items, given the ability to make queries of the form ``does the set $S$ contain any special items?''\ for any subset $S$ of the $n$ items. We give a simple quantum algorithm which uses $O(k)$ queries to solve this problem, as compared with the classical lower bound of $\Omega(k \log(n/k))$ queries.

scholarly journals Impossibility of succinct quantum proofs for collision-freeness

2012 ◽  
Vol 12 (1&2) ◽  
pp. 21-28 ◽  
Author(s):  
Scott Aaronson
Keyword(s):  
Lower Bound ◽  
Quantum Algorithm ◽  
Simple Extension ◽  
Class A ◽  
Collision Problem ◽  
Open Question

We show that any quantum algorithm to decide whether a function f[n] \rightarrow [n] is a permutation or far from a permutation\ must make \Omega( n^{1/3}/w) queries to f, even if the algorithm is given a w-qubit quantum witness in support of f being a permutation. This implies that there exists an oracle A such that {SZK}^{A}\not \subset {QMA}^{A}, answering an eight-year-old open question of the author. \ Indeed, we show that relative to some oracle, {SZK} is not in the counting class {A}_0{PP} defined by Vyalyi. The proof is a fairly simple extension of the quantum lower bound for the collision problem.

Algorithms With Superpolynomial Speed-Up

2006 ◽  
Author(s):  
Phillip Kaye ◽  
Raymond Laflamme ◽  
Michele Mosca
Keyword(s):  
Quantum Algorithms ◽  
Discrete Logarithm ◽  
Quantum Algorithm ◽  
Estimation Algorithm ◽  
Discrete Logarithm Problem ◽  
Phase Estimation ◽  
Post Process ◽  
Classical Algorithm ◽  
Speed Up ◽  
Hadamard Gate

In this chapter we examine one of two main classes of algorithms: quantum algorithms that solve problems with a complexity that is superpolynomially less than the complexity of the best-known classical algorithm for the same problem. That is, the complexity of the best-known classical algorithm cannot be bounded above by any polynomial in the complexity of the quantum algorithm. The algorithms we will detail all make use of the quantum Fourier transform (QFT). We start off the chapter by studying the problem of quantum phase estimation, which leads us naturally to the QFT. Section 7.1 also looks at using the QFT to find the period of periodic states, and introduces some elementary number theory that is needed in order to post-process the quantum algorithm. In Section 7.2, we apply phase estimation in order to estimate eigenvalues of unitary operators. Then in Section 7.3, we apply the eigenvalue estimation algorithm in order to derive the quantum factoring algorithm, and in Section 7.4 to solve the discrete logarithm problem. In Section 7.5, we introduce the hidden subgroup problem which encompasses both the order finding and discrete logarithm problem as well as many others. This chapter by no means exhaustively covers the quantum algorithms that are superpolynomially faster than any known classical algorithm, but it does cover the most well-known such algorithms. In Section 7.6, we briefly discuss other quantum algorithms that appear to provide a superpolynomial advantage. To introduce the idea of phase estimation, we begin by noting that the final Hadamard gate in the Deutsch algorithm, and the Deutsch–Jozsa algorithm, was used to get at information encoded in the relative phases of a state. The Hadamard gate is self-inverse and thus does the opposite as well, namely it can be used to encode information into the phases. To make this concrete, first consider H acting on the basis state |x⟩ (where x ∊ {0, 1}). It is easy to see that You can think about the Hadamard gate as having encoded information about the value of x into the relative phases between the basis states |0⟩ and |1⟩.

scholarly journals Explicit relation between all lower bound techniques for quantum query complexity

2015 ◽  
Vol 13 (04) ◽  
pp. 1350059
Author(s):  
Loïck Magnin ◽  
Jérémie Roland
Keyword(s):  
Lower Bound ◽  
Boolean Functions ◽  
Query Complexity ◽  
Polynomial Method ◽  
Quantum Query Complexity ◽  
State Generation ◽  
Adversary Method ◽  
Explicit Relation ◽  
Special Case ◽  
Quantum Query

The polynomial method and the adversary method are the two main techniques to prove lower bounds on quantum query complexity, and they have so far been considered as unrelated approaches. Here, we show an explicit reduction from the polynomial method to the multiplicative adversary method. The proof goes by extending the polynomial method from Boolean functions to quantum state generation problems. In the process, the bound is even strengthened. We then show that this extended polynomial method is a special case of the multiplicative adversary method with an adversary matrix that is independent of the function. This new result therefore provides insight on the reason why in some cases the adversary method is stronger than the polynomial method. It also reveals a clear picture of the relation between the different lower bound techniques, as it implies that all known techniques reduce to the multiplicative adversary method.

scholarly journals THE DEUTSCH–JOZSA ALGORITHM REVISITED IN THE DOMAIN OF CRYPTOGRAPHICALLY SIGNIFICANT BOOLEAN FUNCTIONS

2005 ◽  
Vol 03 (02) ◽  
pp. 359-370 ◽  
Author(s):  
SUBHAMOY MAITRA ◽  
PARTHA MUKHOPADHYAY
Keyword(s):  
Boolean Function ◽  
Boolean Functions ◽  
Block Cipher ◽  
Quantum Algorithm ◽  
Building Blocks ◽  
Linear Functions ◽  
Constant Time ◽  
Exponential Time ◽  
Classical Algorithm ◽  
Walsh Spectra

Boolean functions are important building blocks in cryptography for their wide application in both stream and block cipher systems. For cryptanalysis of such systems, one tries to find out linear functions that are correlated to the Boolean functions used in the crypto system. Let f be an n-variable Boolean function and its Walsh spectra is denoted by Wf(ω) at the point ω ∈ {0, 1}n. The Boolean function is available in the form of an oracle. We like to find a ω such that Wf(ω) ≠ 0 as this will provide one of the linear functions which are correlated to f. We show that the quantum algorithm proposed by Deutsch and Jozsa7 solves this problem in constant time. However, the best known classical algorithm to solve the problem requires exponential time in n. We also analyze certain classes of cryptographically significant Boolean functions and highlight how the basic Deutsch–Jozsa algorithm performs on them.

Near-optimal Distributed Triangle Enumeration via Expander Decompositions

2021 ◽  
Vol 68 (3) ◽  
pp. 1-36
Author(s):  
Yi-Jun Chang ◽  
Seth Pettie ◽  
Thatchaphol Saranurak ◽  
Hengjie Zhang
Keyword(s):  
Lower Bound ◽  
Positive Integer ◽  
Distributed Algorithms ◽  
Upper Bounds ◽  
Independent Interest ◽  
Generic Framework ◽  
Small Constant

We present improved distributed algorithms for variants of the triangle finding problem in the model. We show that triangle detection, counting, and enumeration can be solved in rounds using expander decompositions . This matches the triangle enumeration lower bound of by Izumi and Le Gall [PODC’17] and Pandurangan, Robinson, and Scquizzato [SPAA’18], which holds even in the model. The previous upper bounds for triangle detection and enumeration in were and , respectively, due to Izumi and Le Gall [PODC’17]. An -expander decomposition of a graph is a clustering of the vertices such that (i) each cluster induces a subgraph with conductance at least and (ii) the number of inter-cluster edges is at most . We show that an -expander decomposition with can be constructed in rounds for any and positive integer . For example, a -expander decomposition only requires rounds to compute, which is optimal up to subpolynomial factors, and a -expander decomposition can be computed in rounds, for any arbitrarily small constant . Our triangle finding algorithms are based on the following generic framework using expander decompositions, which is of independent interest. We first construct an expander decomposition. For each cluster, we simulate algorithms with small overhead by applying the expander routing algorithm due to Ghaffari, Kuhn, and Su [PODC’17] Finally, we deal with inter-cluster edges using recursive calls.

Quantum lower bound for recursive Fourier sampling

2005 ◽  
Vol 5 (2) ◽  
pp. 176-177
Author(s):  
S. Aaronson
Keyword(s):  
Lower Bound ◽  
Fourier Sampling

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