Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms and public availability of first-generation quantum hardware, it is of interest to assess their potential for efficiently handling large quantities of operational data for PHM purposes. This paper addresses the application of quantum kernel classification models for fault detection in wind turbine systems (WTSs). The analyzed data correspond to low-frequency SCADA sensor measurements and recorded SCADA alarm logs, focused on the early detection of pitch fault failures. This work aims to explore potential advantages of quantum kernel methods, such as quantum support vector machines (Q-SVMs), over traditional ML approaches and compare principal component analysis (PCA) and autoencoders (AE) as feature reduction tools. Results show that the proposed quantum approach is comparable to conventional ML models in terms of performance and can outperform traditional models (random forest, k-nearest neighbors) for the selected reduced dimensionality of 19 features for both PCA and AE. The overall highest mean accuracies obtained are 0.945 for Gaussian SVM and 0.925 for Q-SVM models.

/pdf/exploring-quantum-machine-learning-and-feature-reduction-gk8a9yw3.pdf

Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection

In order to improve the security of quantum color images, this study proposes a novel algorithm for quantum color image encryption based on three-dimensional chaotic systems. The encrypted quantum color image is represented by the novel quantum image representation for color digital image model. The original image is first perturbed by the Arnold transform and then the RGB channel is perturbed separately from the chaotic sequence generated by the new three-dimensional chaotic system by the xor operation. Evaluation metrics such as histogram, information entropy, and correlation of neighboring pixels of the image are analyzed using MATLAB. The simulation results show that the pixel values of the encrypted image are uniformly distributed and the algorithm enhances the security of quantum color images. The specific quantum circuit diagram of the encryption algorithm is given in the paper. The superposition and entanglement properties of quantum physics greatly improve the efficiency of complex image processing algorithms, and the overall complexity of the circuit is [Formula: see text], which is efficient and implementable.

Quantum color image encryption based on a novel 3D chaotic system

In order to increase the security and robustness of quantum images, this study combined the quantum DNA codec with quantum Hilbert scrambling to offer an enhanced quantum image encryption technique. Initially, to accomplish pixel-level diffusion and create enough key space for the picture, a quantum DNA codec was created to encode and decode the pixel color information of the quantum image using its special biological properties. Second, we used quantum Hilbert scrambling to muddle the image position data in order to double the encryption effect. In order to enhance the encryption effect, the altered picture was then employed as a key matrix in a quantum XOR operation with the original image. The inverse transformation of the encryption procedure may be used to decrypt the picture since all the quantum operations employed in this research are reversible. The two-dimensional optical image encryption technique presented in this study may significantly strengthen the anti-attack of quantum picture, according to experimental simulation and result analysis. The correlation chart demonstrates that the average information entropy of the RGB three channels is more than 7.999, the average NPCR and UACI are respectively 99.61% and 33.42%, and the peak value of the ciphertext picture histogram is uniform. It offers more security and robustness than earlier algorithms and can withstand statistical analysis and differential assaults.

/pdf/quantum-image-encryption-based-on-quantum-dna-codec-and-2clt77sp.pdf

Quantum Image Encryption Based on Quantum DNA Codec and Pixel-Level Scrambling

Recently, quantum image encryption algorithms are attracting more and more attention, due to the upcoming quantum threat problem to the current cryptographic encryption algorithms with the rapid progress toward the quantum computer production. The aim of this paper is to introduce a self-adaptive encryption scheme to protect quantum image efficiently with minimal storage requirements. The methodology is to use two rounds of encryption with two different pseudorandom number sequences which are obtained from a new designed pseudorandom number generator (PRNG). This PRNG consists of two parts. The first part is based on iterating a recently proposed chaotic-based parallel keyed hash function which is used as a controller for a second part. The second part is a multiplication of Tent and Chebyshev chaotic maps (TCM). This combination of hash function and chaotic maps provides high randomness and a dramatical increase in the control parameters and initial values number which achieves extremely large key space and so makes the scheme stronger against brute force attacks. The seed or initial value of PRNG depends on the input image itself which makes the scheme self-adaptive and so it is stronger against chosen plaintext attacks and known-plaintext attacks. In the first round of encryption, the value of each pixel qubits of the input image is changed to a new value by XORing it with the corresponding qubits of the first pseudorandom sequence using CNOT and Toffoli quantum gates then shifting to the next qubit with the Swap quantum gate. In this round, the pixel value is changed. In the second round, diffusion of the changed pixel value is extended to each pixel in the input image using the second pseudorandom sequence and Toffoli quantum gates. The time complexity of the proposed scheme is less than many recently published quantum image encryption schemes. The scheme security analysis is discussed, and the experimental results proved that the scheme is robust, secure and efficient.

/pdf/quantum-image-encryption-using-a-self-adaptive-hash-function-34yt8zuw.pdf

Quantum Image Encryption Using a Self-Adaptive Hash Function-Controlled Chaotic Map (SAHF-CCM)


 As a branch of quantum secure multi-party computation, quantum private comparison is applied frequently in many fields, such as secret elections, private voting, and identification. A quantum private comparison protocol with higher efficiency and easier implementation is proposed in this paper. The private secrets are encoded as single polarized photons and then encrypted with a homomorphic rotational encryption method. Relying on this method and the circular transmission mode, we implement the multiplexing of photons, raising the efficiency of our protocol to 100%. Our protocol is easy to realize since only single photons, unitary operation, and single-particle measurement are introduced. Meanwhile, the analysis shows that our protocol is also correct and secure.

Efficient quantum private comparison protocol utilizing single photons and rotational encryption

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

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Quantum Image Encryption Based on Quantum Image Decomposition