Improved Quantum Two-Classification Network *

Otmane Ainelkitane, Brian Recktenwall-Calvet, Aasma Iqbal +1 more

Abstract In this study, we investigate a simple quantum pre-processing filter kernel designed with only two CNOT gates for image feature extraction. We examine the impact of these filters when combined with a classical neural network for image classification tasks.Our main hypothesis is that this circuit can extract pixel correlation information that classical filters cannot. This approach is akin to a convolutional neural network, but with quantum layers replacing convolutional layers to extract spatial pixel entanglement. We found that a small circuit with just two CNOT gates can be engineered in three different spatial symmetries, each affecting classification differently. While the filter improves classification when combined with a simple, narrow network, it does not surpass complex classical methods. However, the filter demonstrates potential to enhance classification performance in more sophisticated architectures. Despite this, our empirical results show no clear correlation between the observed improvements and the level of entanglement in the quantum circuit, as measured by Von Neumann Entropy. The underlying cause of this improvement remains unclear and warrants further investigation.

Emre Tokgoz, Sharmendra Desiboyina

G. Bonnell, G. Papini

Suya Chao, Guang Yang, Min Nie

In order to solve the problem of excessive parameters and slow computing speed of classical neural networks, this paper uses the powerful parallel computing power of quantum computing to improve the computing speed of classical neural network models, and proposes a hybrid continuous variational quantum neural network (HCVQNN) model that can be used for network intrusion detection. The continuous variable layer of the model is realized through Gaussian gate and non-Gaussian gate, in which Gaussian gate performs operations such as quantum state weight addition and offset term setting, and non-Gaussian gate performs nonlinear operations, thereby improving the overall expression ability of the network model. In addition, aiming at the unbalanced problem of UNSW-NB15 in the network intrusion dataset, this paper proposes to design the algorithm from the feature level and the algorithm level, using the ReliefF algorithm for feature selection at the feature level, and oversampling and undersampling processing at the algorithm level by combining the Borderline-SMOTE algorithm and GMM algorithm. The classification experimental results on the UNSW-NB15 network intrusion dataset show that compared with the other two network models, the HCVQNN network model obtains higher classification accuracy and lower loss function value in both binary classification and multi-classification tasks, and the classification accuracy for minority categories is also improved.

Qi Feng, Qimei Wang, Lili Xu

This paper introduces a quantum-inspired Qubit neural tree networks with improved Qubit neuron, overlayer connections and different phase operation functions for different neurons to construct disease classification model. A hybrid evolutionary algorithm that combines the modified PIPE algorithm with beetle antennae search is also proposed to optimize the structure and parameters of the Qubit neural tree network. Simulation results on two disease classification problems show that this model has more advantages in classification precision, feature selection and structure simplification, especially for classification with multi-class attributes.

Davide Pastorello