In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal. This can be represented as a maximum clique problem.
In this paper we are dealing with the frequent itemset mining. We concentrate on the special case that we only want to identify the most frequent itemset of length N. To do that, we present a pattern on how to consider this search as an optimization problem. First, we extract the frequency of all possible 2-item-sets. Then the optimization problem is to find the N objects, for which the minimal frequency of all containing 2-item-sets is maximal. This combinatorial optimization problem can be solved by any optimization algorithm. We will solve them with Quantum Annealing and QUBO with QbSolv by D-Wave. The advantages of MFIO in comparison to the state-of-the-art-approach are the enormous reduction of time need, reduction of memory need and the omission of a threshold. The disadvantage is that there is no guaranty for accuracy of the result. The evaluation indicates good results.
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 ${M}_{f}^{(k)}\ensuremath{\approx}{M}_{c}^{(k)}$.
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.
Frequent Itemset Mining (FIM) is an important task in data analytics, where classical algorithms face scalability bottlenecks from the combinatorial growth of candidates and the memory overhead of their data structures. Inspired by recent developments in quantum computing, in this paper, we propose the Quantum Frequent-itemset Mining (QFM) data-processing framework for FIM. Following the level-wise structure of the itemset lattice, QFM introduces three mechanisms: (1) Bit-Vector Qubit Encoding for quantum data representation, which organizes transaction data into branchless bit-vectors to facilitate systematic uncomputation; (2) Mining-Aware Candidate Superposition, which prepares a quantum superposition over valid candidates at each lattice level rather than the full itemset lattice; and (3) Bit-Parallel Threshold Marking, which constructs a logarithmic-depth threshold-marking oracle for reliable repeated support verification within hardware coherence limits. We provide theoretical time complexity analysis, implement QFM on IBM Qiskit and Amazon Braket, and evaluate it on real-world datasets against representative classical baselines, where QFM achieves 96% improvement on average.
Abstract The existing Quantum Algorithm for Association Rules Mining(qARM) need to set the quantum oracle of data artificially when mining frequent itemsets, which is very time-consuming when dealing with large-scale data. Based on the Cn − X gate and extended Grover algorithm (less-than-oracle), this paper proposes an improved method. In the process of calculating support degree, the original quantum phase estimation algorithm is abandoned, and the combined gate based on the Cn − X gate is used to calculate support, so as to improve the accuracy of calculating support. Then, the extended Grover algorithm is modified to screen the support degree. By using IBM’s Qiskit tool for simulation experiments, the data sets with sizes of 2x2 and 4x4 and fragments 29 − 32 items of data for the dataset Market Basket Optimization.The simulation results shown that,when dealing with large-scale data sets, it is no longer necessary to prepare quantum oracle for all data in advance. In the valid data, its search accuracy can reach 90% on average.
Recently, a quantum algorithm for a fundamentally important task in data mining, association rules mining (ARM), called qARM for short, has been proposed. Notably, qARM achieves significant speedup over its classical counterpart for implementing the main task of ARM, i.e., finding frequent itemsets from a transaction database. In this paper, we experimentally implement qARM on both real quantum computers and a quantum computing simulator via the IBM quantum computing platform. In the first place, we design quantum circuits of qARM for a 2$\times 2transactiondatabase(i.e.,atransactiondatabaseinvolvingtwotransactionsandtwoitems),andrunitonfourrealfive−qubitIBMquantumcomputersaswellasonthesimulator.Foralarger4\times $4 transaction database which would lead to circuits with more qubits and a higher depth than the currently accessible IBM real quantum devices can handle, we also construct the quantum circuits of qARM and execute them on “aer_simulator” alone. Both experimental results show that all the frequent itemsets from the two transaction databases are successfully derived as desired, demonstrating the correctness and feasibility of qARM. Our work may serve as a benchmarking, and provide prototypes for implementing qARM for larger transaction databases on both noisy intermediate-scale quantum devices and universal fault-tolerant quantum computers.