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Quantum amplitude amplification and estimation
, 2002
"... Abstract. Consider a Boolean function χ: X → {0, 1} that partitions set X between its good and bad elements, where x is good if χ(x) = 1 and bad otherwise. Consider also a quantum algorithm A such that A0 〉 = � x∈X αxx 〉 is a quantum superposition of the elements of X, and let a denote the proba ..."
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Cited by 174 (14 self)
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Abstract. Consider a Boolean function χ: X → {0, 1} that partitions set X between its good and bad elements, where x is good if χ(x) = 1 and bad otherwise. Consider also a quantum algorithm A such that A0 〉 = � x∈X αxx 〉 is a quantum superposition of the elements of X, and let a denote the probability that a good element is produced if A0 〉 is measured. If we repeat the process of running A, measuring the output, and using χ to check the validity of the result, we shall expect to repeat 1/a times on the average before a solution is found. Amplitude amplification is a process that allows to find a good x after an expected number of applications of A and its inverse which is proportional to 1 / √ a, assuming algorithm A makes no measurements. This is a generalization of Grover’s searching algorithm in which A was restricted to producing an equal superposition of all members of X and we had a promise that a single x existed such that χ(x) = 1. Our algorithm works whether or not the value of a is known ahead of time. In case the value of a is known, we can find a good x after a number of applications of A and its inverse which is proportional to 1 / √ a even in the worst case. We show that this quadratic speedup can also be obtained for a large family of search problems for which good classical heuristics exist. Finally, as our main result, we combine ideas from Grover’s and Shor’s quantum algorithms to perform amplitude estimation, a process that allows to estimate the value of a. We apply amplitude estimation to the problem of approximate counting, in which we wish to estimate the number of x ∈ X such that χ(x) = 1. We obtain optimal quantum algorithms in a variety of settings. 1.
Quantum counting
 In Proceedings of the 25th International Colloquium on Automata, Languages and Programming
, 1998
"... Abstract. We study some extensions of Grover’s quantum searching algorithm. First, we generalize the Grover iteration in the light of a concept called amplitude amplification. Then, we show that the quadratic speedup obtained by the quantum searching algorithm over classical brute force can still be ..."
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Cited by 118 (3 self)
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Abstract. We study some extensions of Grover’s quantum searching algorithm. First, we generalize the Grover iteration in the light of a concept called amplitude amplification. Then, we show that the quadratic speedup obtained by the quantum searching algorithm over classical brute force can still be obtained for a large family of search problems for which good classical heuristics exist. Finally, as our main result, we combine ideas from Grover’s and Shor’s quantum algorithms to perform approximate counting, which can be seen as an amplitude estimation process. 1
On the Power of Quantum Finite State Automata
 Proceedings of the 38th IEEE Conference on Foundations of Computer Science
, 1997
"... In this paper, we introduce 1way and 2way quantum finite state automata (1qfa's and 2qfa's), which are the quantum analogues of deterministic, nondeterministic and probabilistic 1way and 2way finite state automata. We prove the following facts regarding 2qfa's. 1. For any ffl ? 0, ..."
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Cited by 105 (5 self)
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In this paper, we introduce 1way and 2way quantum finite state automata (1qfa's and 2qfa's), which are the quantum analogues of deterministic, nondeterministic and probabilistic 1way and 2way finite state automata. We prove the following facts regarding 2qfa's. 1. For any ffl ? 0, there is a 2qfa M which recognizes the nonregular language L = fa m b m j m 1g with (onesided) error bounded by ffl, and which halts in linear time. Specifically, M accepts any string in L with probability 1 and rejects any string not in L with probability at least 1 \Gamma ffl. 2. For every regular language L, there is a reversible (and hence quantum) 2way finite state automaton which recognizes L and which runs in linear time. In fact, it is possible to define 2qfa's which recognize the noncontextfree language fa m b m c m jm 1g, based on the same technique used for 1. Consequently, the class of languages recognized by linear time, bounded error 2qfa's properly includes the regular l...
On quantum algorithms for noncommutative hidden subgroups
, 2000
"... Quantum algorithms for factoring and finding discrete logarithms have previously been generalized to finding hidden subgroups of finite Abelian groups. This paper explores the possibility of extending this general viewpoint to finding hidden subgroups of noncommutative groups. We present a quantum ..."
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Cited by 81 (3 self)
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Quantum algorithms for factoring and finding discrete logarithms have previously been generalized to finding hidden subgroups of finite Abelian groups. This paper explores the possibility of extending this general viewpoint to finding hidden subgroups of noncommutative groups. We present a quantum algorithm for the special case of dihedral groups which determines the hidden subgroup in a linear number of calls to the input function. We also explore the difficulties of developing an algorithm to process the data to explicitly calculate a generating set for the subgroup. A general framework for the noncommutative hidden subgroup problem is discussed and we indicate future research directions.
Quantum Algorithms for Element Distinctness
 SIAM Journal of Computing
, 2001
"... We present several applications of quantum amplitude amplification to finding claws and collisions in ordered or unordered functions. Our algorithms generalize those of Brassard, Høyer, and Tapp, and imply an O(N 3/4 log N) quantum upper bound for the element distinctness problem in the comparison c ..."
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Cited by 75 (9 self)
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We present several applications of quantum amplitude amplification to finding claws and collisions in ordered or unordered functions. Our algorithms generalize those of Brassard, Høyer, and Tapp, and imply an O(N 3/4 log N) quantum upper bound for the element distinctness problem in the comparison complexity model. This contrasts with Θ(N log N) classical complexity. We also prove a lower bound of Ω ( √ N) comparisons for this problem and derive bounds for a number of related problems. 1
Quantum query complexity of some graph problems
 Proceedings of the 31st International Colloquium on Automata, Lanaguages, and Programming
, 2004
"... Quantum algorithms for graph problems are considered, both in the adjacency matrix model and in an adjacency listlike array model. We give almost tight lower and upper bounds for the bounded error quantum query complexity of Connectivity, Strong Connectivity, Minimum Spanning Tree, and Single Sourc ..."
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Cited by 58 (3 self)
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Quantum algorithms for graph problems are considered, both in the adjacency matrix model and in an adjacency listlike array model. We give almost tight lower and upper bounds for the bounded error quantum query complexity of Connectivity, Strong Connectivity, Minimum Spanning Tree, and Single Source Shortest Paths. For example we show that the query complexity of Minimum Spanning Tree is in Θ(n 3/2) in the matrix model and in Θ ( √ nm) in the array model, while the complexity of Connectivity is also in Θ(n 3/2) in the matrix model, but in Θ(n) in the array model. The upper bounds utilize search procedures for finding minima of functions under various conditions.
Quantum search on boundederror inputs
 In Proc. of 30th ICALP
, 2003
"... Abstract. Suppose we have n algorithms, quantum or classical, each computing some bitvalue with bounded error probability. We describe a quantum algorithm that uses O ( √ n) repetitions of the base algorithms and with high probability finds the index of a 1bit among these n bits (if there is such ..."
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Cited by 54 (5 self)
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Abstract. Suppose we have n algorithms, quantum or classical, each computing some bitvalue with bounded error probability. We describe a quantum algorithm that uses O ( √ n) repetitions of the base algorithms and with high probability finds the index of a 1bit among these n bits (if there is such an index). This shows that it is not necessary to first significantly reduce the error probability in the base algorithms to O(1/poly(n)) (which would require O ( √ nlog n) repetitions in total). Our technique is a recursive interleaving of amplitude amplification and errorreduction, and may be of more general interest. Essentially, it shows that quantum amplitude amplification can be made to work also with a boundederror verifier. As a corollary we obtain optimal quantum upper bounds of O ( √ N) queries for all constantdepth ANDOR trees on N variables, improving upon earlier upper bounds of O ( √ Npolylog(N)). 1
Efficient quantum algorithms for some instances of the nonAbelian hidden subgroup problem
 INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE
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Quantum Algorithm for the Collision Problem
, 1997
"... In this note, we give a quantum algorithm that finds collisions in arbitrary rtoone functions after only O( 3 p N=r ) expected evaluations of the function. Assuming the function is given by a black box, this is more efficient than the best possible classical algorithm, even allowing probabil ..."
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Cited by 47 (1 self)
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In this note, we give a quantum algorithm that finds collisions in arbitrary rtoone functions after only O( 3 p N=r ) expected evaluations of the function. Assuming the function is given by a black box, this is more efficient than the best possible classical algorithm, even allowing probabilism. We also give a similar algorithm for finding claws in pairs of functions. Furthermore, we exhibit a spacetime tradeoff for our technique. Our approach uses Grover's quantum searching algorithm in a novel way. 1 Introduction A collision for function F : X ! Y consists of two distinct elements x 0 ; x 1 2 X such that F (x 0 ) = F (x 1 ). The collision problem is to find a collision in F under the promise that there is one. This problem is of particular interest for cryptology because some functions known as hash functions are used in various cryptographic protocols. The security of these protocols depends crucially on the presumed difficulty of finding Supported in part by Canada's...