With the growth of quantum computing hardware technology, more developers are becoming involved in the design and development of quantum algorithms, leading to the creation of practical applications that align with real-world needs through the combination of quantum hardware and software. Among quantum algorithms, the most well-known include Grover’s algorithm and Shor’s algorithm. Grover’s algorithm can quickly provide search results, making it highly valuable for search-related applications. In light of this, this study primarily examines the theoretical usability of Grover’s algorithm and explores its limitations. Additionally, on the practical side, this research implements Grover’s algorithm using IBM Quantum Computing and the IBM Qiskit package. By comparing simulation results with those obtained from execution on real quantum computers, the study discusses the current limitations of quantum computing hardware technology.
With the growth of quantum computing hardware technology, more developers are becoming involved in the design and development of quantum algorithms, leading to the creation of practical applications that align with real-world needs through the combination of quantum hardware and software. Among quantum algorithms, the most well-known include Grover’s algorithm and Shor’s algorithm. Grover’s algorithm can quickly provide search results, making it highly valuable for search-related applications. In light of this, this study primarily examines the theoretical usability of Grover’s algorithm and explores its limitations. Additionally, on the practical side, this research implements Grover’s algorithm using IBM Quantum Computing and the IBM Qiskit package. By comparing simulation results with those obtained from execution on real quantum computers, the study discusses the current limitations of quantum computing hardware technology.
The focus of this literature review is to examine the foundational principles and current advancements in the field of quantum computing, showcasing its potential to address challenges faced by traditional systems. This study concentrates on key concepts like superposition and entanglement, leading to an exploration of various quantum algorithms, such as Grover’s algorithm and Shor’s algorithm. By comparing Grover’s Search algorithm with binary search, the study aims to demonstrate quantum computing’s advantages in terms of efficiency and speed, especially for large datasets and unsorted databases. The comparison reveals the current state of quantum hardware and its limitations. Despite the challenges associated with hardware requirements, IBM has developed a quantum machine with a 456- qubit quantum processor, marking a milestone and showcasing rapid evolution in this field. Insights gained from this comparison include the potential for algorithms to handle scaled datasets, various applications in data science, and the capability to solve complex problems.
Abstract Quantum computing can solve some classes of problems faster than classical computing. Among the greatest quantum algorithms to showcase this benefit is Grover's search algorithm, which has a quadratic speed-up for unstructured search problems. In this study, we present an implementation of Grover’s algorithm on a large-scale dataset comprising approximately 131,072 (217) names. We contrast its efficiency with the classical linear search based on time complexity, scalability, and the likelihood of success. The experiment was conducted using Qiskit on a quantum simulator, where several query scenarios were implemented to verify the algorithm's efficiency and accuracy. Our findings highlight the theoretical advantage of Grover’s algorithm in situations involving large data sets, while also discussing real-world limitations such as gate noise, qubit number, and circuit depth as we move towards actual quantum hardware. This study serves as a step toward evaluating the real-world relevance of quantum search algorithms and provides information on current challenges and future directions in quantum information retrieval.
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.
Quantum computing represents a paradigm shift in computational ability, offering exponentially faster processing for specific problems than traditional classical algorithms. This review provides a comprehensive assessment of the development and implications of algorithms specifically designed for quantum computing systems. Key quantum algorithms such as Shor’s algorithm for integer factorization and Grover’s algorithm for unstructured search have demonstrated potential quantum supremacy by outperforming their classical counterparts. We examine the theoretical underpinnings and practical challenges of implementing these algorithms, alongside exploring novel algorithmic frameworks emerging from quantum machine learning and error correction disciplines. Furthermore, the review discusses the profound implications of quantum algorithm development in cryptography, optimization, and simulation, which are poised to transform industries reliant on these computational tasks. Our analysis concludes with a perspective on future research directions and the barriers that must be overcome to realize the full potential of quantum algorithms in practical applications.
Quantum computing is a quickly growing field of research thanks to recent hardware advances. The quantum mechanical properties of quantum computers allow them to solve certain families of problems faster than classical computers. A quantum algorithm solving such a problem is Grover's algorithm, which finds an element in an unordered set faster than any classical search algorithm. In this paper an implementation of a 4-qubit Grover's algorithm for the IBM Q computer ibmqx5 is presented. Executing the implementation on an ibmqx5 simulator yield results in line with the theoretically optimal results. The accuracy of the ibmqx5 simulation results compared to the ibmqx5 execution results suggests that current hardware is not yet suitable,due to required complexity of the circuits for a 4-qubit Grover implementation.
Nowadays, quantum computing has emerged as a global technology trend, attracting active research worldwide. One of the most popular quantum algorithms is the quantum search algorithm, commonly known as Grover's algorithm, for unstructured search purposes. This paper presents the implementation of the algorithm with a search space of 3 to 10 qubits using the Python-based Qiskit tool developed by IBM. The implementation results on the IBM QSAM simulator and on the IBM Eagle r3 quantum hardware are analyzed and compared. This paper also provides results with circuits of varying iteration numbers. Finally, the paper discusses the advantages of this quantum search algorithm compared to traditional methods, and the obstacles towards realizing its hardware implementation.