The problem of searching for a given substring in the text was considered. It is known that classical algorithms solve this problem in a linear time depending on the length of the text and the specified template. Quantum algorithms speed up the search by “square root times”. In this paper, we proposed a quantum algorithm that solves the search problem a) with a high probability of getting the correct result and b) with the same acceleration (by “square root times”) as compared with the classical one, but it c) requires much less memory (based on the number of qubits used) than the previously known quantum algorithms.
The problem of searching for a given substring in the text was considered. It is known that classical algorithms solve this problem in a linear time depending on the length of the text and the specified template. Quantum algorithms speed up the search by “square root times”. In this paper, we proposed a quantum algorithm that solves the search problem a) with a high probability of getting the correct result and b) with the same acceleration (by “square root times”) as compared with the classical one, but it c) requires much less memory (based on the number of qubits used) than the previously known quantum algorithms.
The paper considers the problem of finding a given substring in a text. It is known that the complexity of a classical search query in an unordered database is linear in the length of the text and a given substring. At the same time, Grover’s quantum search provides a quadratic speed-up in the complexity of the query and gives the correct result with a high probability. We propose a hybrid classical–quantum algorithm (hybrid random–quantum algorithm, to be more precise) that implements Grover’s search to find a given substring in a text. As expected, the algorithm works (a) with a high probability of obtaining the correct result and (b) with a quadratic query acceleration compared to the classical one. What is new is that our algorithm uses the uniform hash family functions technique. As a result, our algorithm is much more memory efficient (in terms of the number of qubits used) compared to previously known quantum algorithms.
Abstract In this paper, we consider the problem of searching for an element in a dictionary. Various approaches to solving this problem have been proposed in recent decades: classical and quantum algorithms. We present two algorithms: a hybrid classical-quantum algorithm [1], that implements Grover’s search, and a “pure” quantum algorithm based on the quantum fingerprinting technique. Both algorithms work (a) with a high probability of obtaining the correct result and (b) with a quadratic query acceleration compared to the classical one. Our algorithms are much more memory efficient in terms of the number of qubits used compared to previously known quantum algorithms.
In this work, we present a quantum query algorithm for searching a word of length m in an unsorted dictionary of size n. The algorithm uses O(n) queries (Grover operators), like previously known algorithms. What is new is that the algorithm is based on the quantum fingerprinting-hashing technique, which (a) provides a first level of amplitude amplification before applying the sequence of Grover amplitude amplification operators and (b) makes the algorithm more efficient in terms of memory use -- it requires O(logn+logm) qubits. Note that previously developed algorithms by other researchers without hashing require O(logn+m) qubits.
We consider the problem of searching for an element in a dictionary. Various approaches to solving this problem have been proposed in recent decades: classical and quantum algorithms. We present two algorithms: a hybrid classical-quantum algorithm, which implements Grover’s search, and a “pure” quantum algorithm based on the quantum fingerprinting technique. Both algorithms work with a high probability of obtaining the correct result and with a quadratic query acceleration compared to the classical one. Our algorithms are much more memory efficient in terms of the number of qubits used compared to previously known quantum algorithms.
We study quantum algorithms for several fundamental string problems, including Longest Common Substring, Lexicographically Minimal String Rotation, and Longest Square Substring. These problems have been widely studied in the stringology literature since the 1970s, and are known to be solvable by near-linear time classical algorithms. In this work, we give quantum algorithms for these problems with near-optimal query complexities and time complexities. Specifically, we show that: - Longest Common Substring can be solved by a quantum algorithm in O~(n2/3) time, improving upon the recent O~(n5/6)-time algorithm by Le Gall and Seddighin (2020). Our algorithm uses the MNRS quantum walk framework, together with a careful combination of string synchronizing sets (Kempa and Kociumaka, 2019) and generalized difference covers. - Lexicographically Minimal String Rotation can be solved by a quantum algorithm in n1/2+o(1) time, improving upon the recent O~(n3/4)-time algorithm by Wang and Ying (2020). We design our algorithm by first giving a new classical divide-and-conquer algorithm in near-linear time based on exclusion rules, and then speeding it up quadratically using nested Grover search and quantum minimum finding. - Longest Square Substring can be solved by a quantum algorithm in O~(n) time. Our algorithm is an adaptation of the algorithm by Le Gall and Seddighin (2020) for the Longest Palindromic Substring problem, but uses additional techniques to overcome the difficulty that binary search no longer applies. Our techniques naturally extend to other related string problems, such as Longest Repeated Substring, Longest Lyndon Substring, and Minimal Suffix.
We study quantum algorithms for several fundamental string problems, including Longest Common Substring, Lexicographically Minimal String Rotation, and Longest Square Substring. These problems have been widely studied in the stringology literature since the 1970s, and are known to be solvable by near-linear time classical algorithms. In this work, we give quantum algorithms for these problems with near-optimal query complexities and time complexities. Specifically, we show that: Longest Common Substring can be solved by a quantum algorithm in Õ(n2/3) time, improving upon the recent Õ(n5/6)-time algorithm by Le Gall and Seddighin (2020). Our algorithm uses the MNRS quantum walk framework, together with a careful combination of string synchronizing sets (Kempa and Kociumaka, 2019) and generalized difference covers. Lexicographically Minimal String Rotation can be solved by a quantum algorithm in n1/2 + o(1) time, improving upon the recent Õ(n3/4)-time algorithm by Wang and Ying (2020). We design our algorithm by first giving a new classical divide-and-conquer algorithm in near-linear time based on exclusion rules, and then speeding it up quadratically using nested Grover search and quantum minimum finding. Longest Square Substring can be solved by a quantum algorithm in time. Our algorithm is an adaptation of the algorithm by Le Gall and Seddighin (2020) for the Longest Palindromic Substring problem, but uses additional techniques to overcome the difficulty that binary search no longer applies. Our techniques naturally extend to other related string problems, such as Longest Repeated Substring, Longest Lyndon Substring, and Minimal Suffix.