Pattern Classifications Using Grover’s and Ventura’s Algorithms in a Two-qubits System

Quantum Pattern Classification in a Three-Qubit System

“…However, it has been shown in [13][14][15]] that Grover's operator is more effective in cases where the number of patterns in the input dataset is large, which obviously contradicted Ventura's claim. In [13,14], the effects of applying Grover's [8] and Ventura's [9] algorithms were tested on a two-qubit system, while, in [15], this was tested on a three-qubit system. The effects of Grover's and Ventura's algorithms have been studied over three input state types to determine which is more suitable for pattern classification.…”

“…It was not only one of the first and most important contributions to quantum machine learning, but Ventura also claimed to provide a higher probability of success than Grover's algorithm. In [13][14][15], these quantum search algorithms (Grover's algorithm and Ventura's algorithm) are used to classify a pre-defined pattern in an unstructured dataset in two-and three-qubit systems; the classification probabilities were calculated when the two algorithms were applied to three input superposition types. However, it has been shown in [13][14][15]] that Grover's operator is more effective in cases where the number of patterns in the input dataset is large, which obviously contradicted Ventura's claim.…”

“…In [13][14][15], these quantum search algorithms (Grover's algorithm and Ventura's algorithm) are used to classify a pre-defined pattern in an unstructured dataset in two-and three-qubit systems; the classification probabilities were calculated when the two algorithms were applied to three input superposition types. However, it has been shown in [13][14][15]] that Grover's operator is more effective in cases where the number of patterns in the input dataset is large, which obviously contradicted Ventura's claim. In [13,14], the effects of applying Grover's [8] and Ventura's [9] algorithms were tested on a two-qubit system, while, in [15], this was tested on a three-qubit system.…”

Binary Classification Quantum Neural Network Model Based on Optimized Grover Algorithm

“…The Binary QNN Model in this paper is a kind of model based on the Grover algorithm and the QNN supervised learning algorithm. First of all, this is based on the traditional Grover algorithm being analyzed and improved, using Younes’ algorithm to improve the search algorithm efficiency, inserting this into the iterative learning process of the quantum neural network [ 21 , 22 ], and using quantum processes to promote the efficiency of the algorithm and neural network to realize multiple network synchronization searching and learning [ 23 , 24 ] for each iteration algorithm to improve the efficiency of solutions [ 25 ]. The quantum neural network learning scheme in this paper can be applied to quickly find the user’s IP address in massive weblogs, and then accurately and efficiently identifying and classifying relevant and valuable information such as the IP addresses of network logs.…”