Recent advances in the quantum computing field of computer science that apply the power of quantum mechanics to perform calculations have led many companies, such as IBM, Google, Microsoft, D-Wave Systems, and Xanadu Quantum Technologies, to invest in the field of quantum computing. The main goal of this paper is to apply the Elgendy et al. algorithm, which proposes quantum algorithms for set operations such as true intersection, false intersection, difference, and union, on the current viability of available IBM quantum computers. Moreover, this paper includes a new 5-qubit implementation of Elgendy et al., tests results showing the current capabilities of quantum computers, extends to greater scales, and evaluates algorithm performance on available noisy devices. Elgendy et al. algorithms use a two-stage quantum amplitude amplification technique: The first stage involves preparing the truth set of the first Boolean function using incomplete superpositions, which is done using the Younes et al. technique which performs quantum searching via entanglement and partial diffusion. In the second stage, set operations are handled using an oracle that represents the second Boolean function and a modified version of Arima's algorithm. Among the potential uses are machine learning, cryptography, pattern recognition, and database querying.
Recent advances in the quantum computing field of computer science that apply the power of quantum mechanics to perform calculations have led many companies, such as IBM, Google, Microsoft, D-Wave Systems, and Xanadu Quantum Technologies, to invest in the field of quantum computing. The main goal of this paper is to apply the Elgendy et al. algorithm, which proposes quantum algorithms for set operations such as true intersection, false intersection, difference, and union, on the current viability of available IBM quantum computers. Moreover, this paper includes a new 5-qubit implementation of Elgendy et al., tests results showing the current capabilities of quantum computers, extends to greater scales, and evaluates algorithm performance on available noisy devices. Elgendy et al. algorithms use a two-stage quantum amplitude amplification technique: The first stage involves preparing the truth set of the first Boolean function using incomplete superpositions, which is done using the Younes et al. technique which performs quantum searching via entanglement and partial diffusion. In the second stage, set operations are handled using an oracle that represents the second Boolean function and a modified version of Arima's algorithm. Among the potential uses are machine learning, cryptography, pattern recognition, and database querying.
Abstract Analyzing the relations between Boolean functions has many applications in many fields, such as database systems, cryptography, and collision problems. This paper proposes four quantum algorithms that use amplitude amplification techniques to perform set operations, including Intersection, Difference, and Union, on two Boolean functions in O(N) O ( N ) time complexity. The proposed algorithms employ two quantum amplitude amplification techniques divided into two stages. The first stage uses the Younes et al. algorithm for quantum searching via entanglement and partial diffusion to prepare incomplete superpositions of the truth set of the first Boolean function. In the second stage, a modified version of Arima’s algorithm, along with an oracle that represent the second Boolean function, is employed to handle the set operations. The proposed algorithms have a higher probability of success in more general and comprehensive applications when compared with relevant techniques in literature.
This paper focuses on testing the current viability of using quantum computers for the processing of data-driven tasks fueled by emerging data science applications. We test the publicly available IBM quantum computers using Grover's algorithm, a well-known quantum search algorithm, to obtain a baseline for the general evaluations of these quantum devices and to investigate the impacts of various factors such as number of quantum bits (or qubits), qubit choice, and device choice. The main contributions of this paper include a new 4-qubit implementation of Grover's algorithm and test results showing the current capabilities of quantum computers. Our study indicates that quantum computers can currently only be used accurately for solving simple problems with very small amounts of data. There are also notable differences between different selections of the qubits in the implementation design and between different quantum devices that execute the algorithm.
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.
In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms have the proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community has already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the join order optimization problem in databases.
The K-SAT problem is a fundamental challenge in computational theory, widely used in artificial intelligence, cryptography, and optimization. Classical algorithms struggle with exponential time complexity, making them inefficient for large-scale instances. In this research, we propose an efficient quantum computing approach based on Grover’s algorithm to enhance the search process for satisfiable solutions in K-SAT problems. Our implementation leverages quantum superposition and amplitude amplification to explore multiple possible solutions simultaneously, reducing the search complexity from O (2ⁿ) in classical methods to O(√2ⁿ) in quantum computing. We design a quantum oracle that encodes the K-SAT clauses and integrates it into Grover’s iterative search framework. The performance is evaluated through Qiskit simulations, demonstrating a significant improvement in search efficiency compared to classical brute-force techniques. The results highlight the potential of quantum algorithms in solving complex combinatorial problems with enhanced speed and accuracy. This study contributes to the development of quantum-accelerated optimization methods, paving the way for real-world applications in machine learning, cryptanalysis, and large-scale data processing.
Motivated by its unique capabilities, quantum computation has gained significant attention over the last decade with numerous models and algorithms proposed for dealing with engineering challenges.The field of risk and reliability has also seen a growing interest in this area, with studies exploring Quantum Machine Learning for remaining useful life, Quantum Optimization for condition monitoring in civil structures, and Quantum Inference for enhancing Bayesian network models, to name a few.However, a common limitation across these works is the lack of thorough comparisons between the proposed quantum algorithms and their state-of-the-art classical counterparts.This critical gap must be addressed not only to evaluate the field's current state reliably but also to guide its development toward the most promising paths for achieving a quantum advantage.There are two key challenges to addressing this gap.First, quantum computation operates on fundamentally different principles than traditional computing, making direct comparisons -such as using the number of iterations -often infeasible.Second, large-scale, error-corrected quantum computers are not yet available, so machine-to-machine comparisons are also not yet possible.In this paper, we detail a novel methodology to evaluate quantum algorithms against their classical counterparts.Our technique is based on a simple observation: quantum computers do not extend the operations that a classical computer can perform.Instead, they have the potential to make them more efficient.As such, when large problems are considered, they ought to present a shorter runtime than classical algorithms to surpass, in any sense of the word, a classical algorithm.We validate the proposed methodology by applying it to an exciting application of quantum computation within the field of system reliability: the identification of minimal cut sets via the leverage of the Grover algorithm and Quantum Amplitude Amplification.