The rapid development of big data puts forward higher requirements on computational efficiency and storage capacity. But the traditional mining algorithms based on classical computing were unable to satisfy the demand of data analysis and computing. Because of quantum systems unique ability of hyper parallel computation and hyper acceleration, the large-scale computing and data storage can be solve well. Although the development of quantum computer has not yet achieved a qualitative leap, quantum computing in big data application has achieved lots of theoretical achievements. In this paper, we make quantum data mining as a starting point. Since we have no sufficient space to store candidate sets of association rules and have low computation ability in the processing of association rules, we propose a novel quantum method of data storage and retrieval for association rules mining based on Boolean matrix, we propose the algorithm called Q-Eclat can accelerate the computation of candidate sets support. According to our analysis based on open data source, the proposed method outperforms the classical Eclat algorithm in terms of storage capacity and computing ability.
The rapid development of big data puts forward higher requirements on computational efficiency and storage capacity. But the traditional mining algorithms based on classical computing were unable to satisfy the demand of data analysis and computing. Because of quantum systems unique ability of hyper parallel computation and hyper acceleration, the large-scale computing and data storage can be solve well. Although the development of quantum computer has not yet achieved a qualitative leap, quantum computing in big data application has achieved lots of theoretical achievements. In this paper, we make quantum data mining as a starting point. Since we have no sufficient space to store candidate sets of association rules and have low computation ability in the processing of association rules, we propose a novel quantum method of data storage and retrieval for association rules mining based on Boolean matrix, we propose the algorithm called Q-Eclat can accelerate the computation of candidate sets support. According to our analysis based on open data source, the proposed method outperforms the classical Eclat algorithm in terms of storage capacity and computing ability.
Association rules mining (ARM) is one of the most important problems in knowledge discovery and data mining. Given a transaction database that has a large number of transactions and items, the task of ARM is to acquire consumption habits of customers by discovering the relationships between itemsets (sets of items). In this paper, we address ARM in the quantum settings and propose a quantum algorithm for the key part of ARM, finding frequent itemsets from the candidate itemsets and acquiring their supports. Specifically, for the case in which there are Mf(k) frequent k-itemsets in the Mc(k) candidate k-itemsets (${M}_{f}^{(k)}\ensuremath{\le}{M}_{c}^{(k)}$), our algorithm can efficiently mine these frequent k-itemsets and estimate their supports by using parallel amplitude estimation and amplitude amplification with complexity $O(\frac{k\sqrt{{M}_{c}^{(k)}{M}_{f}^{(k)}}}{\ensuremath{\epsilon}})$, where $\ensuremath{\epsilon}$ is the error for estimating the supports. Compared with the classical counterpart, i.e., the classical sampling-based algorithm, whose complexity is $O(\frac{k{M}_{c}^{(k)}}{{\ensuremath{\epsilon}}^{2}})$, our quantum algorithm quadratically improves the dependence on both $\ensuremath{\epsilon}$ and Mc(k) in the best case when ${M}_{f}^{(k)}\ensuremath{\ll}{M}_{c}^{(k)}$ and on $\ensuremath{\epsilon}$ alone in the worst case when ${M}_{f}^{(k)}\ensuremath{\approx}{M}_{c}^{(k)}$.
With the explosion of big data, higher requirements for computational efficiency have emerged. Compared with classical computing, quantum computing possesses quantum parallelism due to the unique nature of quantum systems. It has been found that many classical algorithms can be accelerated using quantum computing. In addition to factorizing a large integer, quantum computers can be used for data processing and analysis. In recent years, two frontiers, i.e., big data and quantum computing have begun to merge. Though practical quantum computers have not yet been built, theoretical studies have made some important progress. In this review, we introduce the basic principles of quantum computing. As a representative example, we describe the Grover search algorithm and its important generalizations. Quantum machine learning is the entry point for the integration of big data with quantum computation. We review in detail, the applications of quantum computation in data mining, the main application of machine learning. Other aspects of quantum computing in big data are also briefly summarized.
The vast amount of data generated by various applications necessitates the need for advanced computing capabilities to process, analyze and extract insights from it. Quantum computing, with its ability to perform complex operations in parallel, holds immense promise for data mining in cloud environments. This article examines cutting-edge methods for using quantum computing for data mining. The paper analyzes several key quantum algorithms, including Grover's search algorithm, quantum principal component analysis (QPCA), and quantum support vector machines (QSVM). It delves into the details of these algorithms, exploring their principles, applications, and potential benefits in various domains. We also done the comparative analysis of various algorithms and discussed about the difficulties of using quantum computing for data mining, such as the requirement for specialized knowledge, scalability issues, and hardware constraints. Overall, this work demonstrates the ability of quantum computing for scalable and effective data mining in cloud systems and proposes future research avenues for investigating the use of quantum computing for data mining.
Technological advancements in the 21st century have led to the rise of "big data," characterized by datasets so vast and complex that traditional database systems struggle to manage them. This term denotes datasets that are too vast and complex to be processed by traditional database systems. As big data continues to evolve, it introduces significant challenges related to data processing, storage, and analysis. In response, quantum-inspired algorithms have emerged as a promising solution to these challenges. Quantum-inspired algorithms, which draw on principles from quantum computing, present a promising solution to the challenges of big data processing, storage, and analysis. Techniques such as Quantum Annealing, Quantum Circuits and Gates, and Quantum Parallelism leverage quantum principles like superposition and entanglement to enhance data processing efficiency and speed. These methods offer significant improvements over traditional tools like Hadoop, MapReduce, RapidMiner, and R Programming, which are used for distributed storage, processing, and analytics of large datasets. By integrating quantum-inspired algorithms with existing data processing techniques, this study aims to address the limitations of classical methods and advance the field of big data analytics.
Data mining is a key technology in big data analytics and it can discover understandable knowledge (patterns) hidden in large data sets. Association rule is one of the most useful knowledge patterns, and a large number of algorithms have been developed in the data mining literature to generate association rules corresponding to different problems and situations. Privacy becomes a vital issue when data mining is used to sensitive data sets like medical records, commercial data sets and national security. In this Letter, we present a quantum protocol for mining association rules on vertically partitioned databases. The quantum protocol can improve the privacy level preserved by known classical protocols and at the same time it can exponentially reduce the computational complexity and communication cost.
Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.