Distributed computing seems to be a natural approach to overcome size limitations of quantum computers in terms of number of qubits. But one lacks an efficient distribution approach to deal systematically with potential algorithms. This work proposes suitable quantum probes to reduce measurements in distributed sub-systems. Probes are introduced in the context of distributed Grover search. These are shown to be indeed efficient. The latter can be generalized to be applicable to other distributed algorithms of interest.
Distributed computing seems to be a natural approach to overcome size limitations of quantum computers in terms of number of qubits. But one lacks an efficient distribution approach to deal systematically with potential algorithms. This work proposes suitable quantum probes to reduce measurements in distributed sub-systems. Probes are introduced in the context of distributed Grover search. These are shown to be indeed efficient. The latter can be generalized to be applicable to other distributed algorithms of interest.
Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover’s algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we develop a distributed application with a key-value data structure based on Grover’s algorithm called IQuCS . Consider a database with duplicates. By encoding each element to a binary type with a unique key and forming a key-value pair, we can count the number of occurrences of each element in the database based on quantum computing. We have optimized the operation process by filtering data points to make it more efficient. To determine the effect of this optimization, we evaluate it with datasets of different sizes and with different numbers of duplicates. With the assistance of classical computers, IQuCS can reduce the problem set for each query. Due to this reduction, IQuCS requires fewer qubits. Through the iterative management, IQuCS achieves a reduction of qubit virtualized consumption, up to 66.2%, with reasonable accuracy.
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.
Grover’s quantum search algorithm delivers quadratic speedup over classical counterparts for finding an item in unstructured database. But the accuracy of the algorithm degrades as the number of qubits are increased. Noises of various types affect the accuracy of the Grover’s algorithm. The imperfect measurement on the qubit results in measurement errors appears to be one of the factors which limit the scalability of near-term quantum devices/algorithms. This paper is focused on the implementation of measurement error mitigation technique on Grover’s algorithm up to 4-qubits. The measurement error mitigation model for Grover’s algorithm is developed and implemented on the real-time quantum computer. The accuracy of Grover’s algorithm up to 4-qubits with and without measurement errors is evaluated and compared. The results indicate that measurement error mitigation technique mitigates the measurement errors of the Grover’s algorithm and improves its accuracy.
Grover's Algorithm is a prominent quantum search algorithm in the rapidly advancing quantum computing study. By exploiting superposition and quantum parallelism, it has the potential to outperform classical brute-force approaches. Nevertheless, the current practical application of quantum technology in decrypting intricate passwords is restricted by its early stage, namely the restricted qubit quantities and short coherence durations. This work presents an innovative distributed quantum computing paradigm that addresses these difficulties by partitioning the password search space among several quantum processors. Each processor executes Grover's Algorithm on a subset of the search space, minimizing the computational complexity and quantum resource requirements for each individual processor. The partial findings are subsequently transmitted to a central classical computer, which amalgamates them to obtain the ultimate password. This distributed methodology not only allows for practicality with current quantum systems but also provides a scalable paradigm that can adapt to the progress of quantum computer technology. By decomposing the quantum brute-force search problem into smaller, more controllable elements, this approach can significantly decrease the time complexity of password retrieval, which has significant consequences for cryptography and cybersecurity.
The essential operations of a quantum computer can be accomplished using solely optical elements, with different polarization or spatial modes representing the individual qubits. We present a simple all-optical implementation of Grover's algorithm for efficient searching, in which a database of four elements is searched with a single query. By ‘compiling’ the actual set-up, we have reduced the required number of optical elements from 24 to only 12. We discuss the extension to large databases, and the limitations of these techniques.
The essential operations of a quantum computer can be accomplished using solely optical elements, with different polarization or spatial modes representing the individual qubits. We present a simple all-optical implementation of Grover's algorithm for efficient searching, in which a database of four elements is searched with a single query. By ‘compiling’ the actual set-up, we have reduced the required number of optical elements from 24 to only 12. We discuss the extension to large databases, and the limitations of these techniques.