Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.

Implementation of Quantum Annealing: A Systematic Review

Classification of mild cognitive impairment based on handwriting dynamics and qEEG

In this paper, by using entanglement swapping of Bell states, an efficient quantum private comparison(QPC) protocol with a semi-honest party is proposed. The semi-honest third party (TP) is required to help two participants perform the comparison. She can record intermediate results and do some calculations in the whole process of the protocol execution, but she is not allowed to conspire with any participants. Moreover, TP cannot get two participants’ privacy information except the comparison results. The security analysis shows that the proposed protocol can resist both outsider attacks and insider attacks. Compared with other similar QPC protocols, the proposed one needs neither unitary operations nor the entanglement swapping of multiparticle quantum states, and it only needs the entanglement swapping of Bell states, which makes it more practical. Since three-bit classical information could be compared in each comparison, the proposed protocol has a good performance in its efficiency.

Efficient Quantum Private Comparison Based on Entanglement Swapping of Bell States

A neural networks-based numerical method for the generalized Caputo-type fractional differential equations

Texture enhancement is one of the most important techniques in digital image processing and plays an essential role in medical imaging since textures discriminate information. Most image texture enhancement techniques use classical integral order differential mask operators or fractional differential mask operators using fixed fractional order. These masks can produce excessive enhancement of low spatial frequency content, insufficient enhancement of large spatial frequency content, and retention of high spatial frequency noise. To improve upon existing approaches of texture enhancement, we derive an improved Variable Order Fractional Centered Difference (VOFCD) scheme which dynamically adjusts the fractional differential order instead of fixing it. The new VOFCD technique is based on the second order Riesz fractional differential operator using a Lagrange 3-point interpolation formula, for both grey scale and colour image enhancement. We then use this method to enhance photographs and a set of medical images related to patients with stroke and Parkinson’s disease. The experiments show that our improved fractional differential mask has a higher signal to noise ratio value than the other fractional differential mask operators. Based on the corresponding quantitative analysis we conclude that the new method offers a superior texture enhancement over existing methods.

A variable order fractional differential-based texture enhancement algorithm with application in medical imaging

Aiming at the slow processing speed of classic image encryption algorithms and the security analysis of existing quantum image encryption algorithms, this paper combines the representation method of quantum images and proposes a quantum image encryption algorithm based on image correlation decomposition. Using the principle of quantum state superposition and measurement, the association between image pixels is established, the image is decomposed into a series of feature sub-images and stored in a complete binary tree set, and different sub-images are operated and encrypted by random phase operation and quantum rotation operation. Then superimpose all the sub-images to obtain the ciphertext image. The algorithm has a larger key space so that it can resist brute force attacks. At the same time, the quantum encryption algorithm has lower computational complexity than classic encryption algorithms. In addition, because the ciphertext image is transmitted in the communication channel in the form of a quantum state, the security of quantum image encryption also surpasses the security of classical image encryption.

Quantum Image Encryption Based on Quantum Image Decomposition

Emotion recognition is a research hotspot in the field of artificial intelligence. If the human-computer interaction system can sense human emotion and express emotion, it will make the interaction between the robot and human more natural. In this paper, a multimodal emotion recognition model based on many-objective optimization algorithm is proposed for the first time. The model integrates voice information and facial information and can simultaneously optimize the accuracy and uniformity of recognition. This paper compares the emotion recognition algorithm based on many-objective algorithm optimization with the single-modal emotion recognition model proposed in this paper and the ISMS_ALA model proposed by recent related research. The experimental results show that compared with the single-mode emotion recognition, the proposed model has a great improvement in each evaluation index. At the same time, the accuracy of emotion recognition is 2.88% higher than that of the ISMS_ALA model. The experimental results show that the many-objective optimization algorithm can effectively improve the performance of the multimodal emotion recognition model.

/pdf/support-vector-machine-weather-prediction-technology-based-2rzghvcrhi.pdf

Support Vector Machine Weather Prediction Technology Based on the Improved Quantum Optimization Algorithm.

Numerical solutions of wavelet neural networks for fractional differential equations

Application of fractional theory in quantum back propagation neural network

As we all know, the weather forecast is very complex, which is related to the location of the environment, climate, season, and other factors. The accuracy of weather prediction is of great significance for the application of hydrological and agroclimatological research. In this research, the improved quantum genetic algorithm (IQGA) and support vector machine (SVM) are combined to model the rainfall of medium-sized cities in Australia. The daily time scale measured weather information, such as min temperature (MT), evaporation, sunshine, humidity, cloud, and so on, which was used to build the proposed predictive models. Next, the effects of IQGA-SVM, GA-SVM, and other classical machine learning algorithms on the dataset are compared. Experiments show that the IQGA-SVM model has the optimal prediction ability and less running time. Statistical evaluation metrics such as accuracy, area under curve (AUC), and running time were used to validate the model efficiency. The IQGA-SVM model uses IQGA to optimize the parameters of SVM. Compared with the traditional random walk and grid search method, it improves the calculation efficiency and the accuracy of parameter optimization. This parameter optimization method has good expansibility and can be applied to other algorithms that need parameter adjustment to achieve the optimal model prediction effect. The results of this study proved that IQGA-SVM is a reliable modeling technique for forecasting rainfall.

/pdf/corrigendum-to-support-vector-machine-weather-prediction-e0pdzzwi.pdf

Corrigendum to “Support Vector Machine Weather Prediction Technology Based on the Improved Quantum Optimization Algorithm”

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