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Physics of Particles and Nuclei

Efficient Dictionary Search Quantum Algorithms

Farid Mansurovich Ablayev, Nailya Salikhova, Marat Ablayev

Published Aug 1, 2025• 2 citations
Publisher

Abstract

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.

DOI
10.1134/s1063779625700017

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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.
The problem of searching for an element within a dictionary was considered. Over the past decades, various approaches, including classical and quantum algorithms, have been proposed to solve it. One possible solution is the method of quantum amplitude amplification, which underpins the well-known Grover algorithm and enables a quadratic speedup in the search process. In this article, a new approach to searching for an element w of length m in a dictionary V of size n with the use of the quantum fingerprinting function was introduced. The developed algorithm has a query complexity of O(√n) and requires O(logn+logm) qubits.
In this work, we present a quantum query algorithm for searching a word of length mmm in an unsorted dictionary of size nnn. The algorithm uses O(n)O(\sqrt{n})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(log⁡n+log⁡m)O(\log n + \log m)O(logn+logm) qubits. Note that previously developed algorithms by other researchers without hashing require O(log⁡n+m)O(\log n + m)O(logn+m) qubits.
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.
Quantum search is one of the most important algorithmic developments in quantum computing because it demonstrates how quantum principles can improve the efficiency of solving unstructured search problems. Grover’s algorithm provides a quadratic speed-up over classical linear search by reducing query complexity from O(N) to O(√N), where N represents the size of the search space. This review examines the principles, performance, and applications of Grover’s algorithm, with emphasis on its theoretical foundation and practical relevance. The discussion covers the roles of superposition, quantum interference, oracle operations, diffusion operators, and amplitude amplification in increasing the probability of measuring a desired solution. The manuscript also reviews the computational performance of quantum search in comparison with classical search methods and considers its relevance to database searching, combinatorial optimisation, cryptography, machine learning, bioinformatics, quantum chemistry, and materials science. The review highlights that Grover’s algorithm is not limited to database retrieval, as many computationally intensive problems can be reformulated as search or optimisation tasks. However, practical implementation remains constrained by current quantum hardware limitations, including decoherence, quantum noise, limited qubit availability, circuit depth, and the difficulty of designing efficient oracles. Despite these challenges, continuing progress in quantum hardware, error correction, cloud-based quantum platforms, and hybrid quantum-classical architectures suggests that quantum search will remain an important area of research. Overall, Grover’s algorithm represents a foundational quantum algorithm with significant theoretical value and potential long-term application in computational domains involving large and unstructured search spaces.
Search is one of the most commonly used primitives in quantum algorithm design. It is known that quadratic speedups provided by Grover’s algorithm are optimal, and no faster quantum algorithms for Search exist. While it is known that at least some quantum computation is required to achieve these speedups, the existing bounds do not rule out the possibility of an equally fast hybrid quantum-classical algorithm where most of the computation is classical. In this work, we study such hybrid algorithms, and we show that classical computation, unless it by itself can solve the Search problem, cannot assist quantum computation. In addition, we generalize this result to algorithms with subconstant success probabilities.