Quantum Neural Network for Image Watermarking

Wen-Kai Yu

Optical communication has an increasing need for security in public transmission scenarios. Here, we present a protocol for cryptographic key distribution over a public network via a photon-counting compressive imaging system with watermarking, which utilizes a watermarking technique to distribute secure keys, and uses reconstructed images for simultaneous identity authentication and tampering identification. The watermark is embedded in the rearranged compressed measurements of the object, and then the signal is transmitted through a public network. At the receiving terminal, legitimate users can easily extract the watermark as the cryptographic key by using initial keys and the variance characteristic of random measurements. Artificial tampering and attacks can be detected by accurately retrieved images. The realization of this protocol is a step forward toward practical applications, and will be beneficial for the broader fields of optical security in many ways.

Zheng Xing, Xiaochen Yuan, Chan–Tong Lam

Guoming Chen, Qiang Chen, Shun Long +3 more

Ding Qiu-lin

The storage capacity of the conventional Hopfield network is the 0.14 times of the number of neurons(P=0.14N). Because of the huge difficulty in recognizing a large number of images or patterns, researchers are looking for new methods at all times. Quantum neural network (QNN) is a young and outlying science built upon the combination of classical neural network and quantum computing. A Quantum Hopfield neural network (QHNN) whose elements of the storage matrix are distributed in a probability way on the base of quantum linear superposition is presented for speeding up the images recognition and increasing the number of the images recognition. Contrasting to the conventional Hopfield neural network, the storage capacity of the QHNN is increased by a factor of 2N, where N is the number of neurons. Besides, the case analysis and simulation tests have been carried out for the recognition of images in this paper. The result indicates that QHNN can recognize the images or patterns effectively and its working process accords with quantum evolvement process.

G. Bonnell, G. Papini

Rui Fu, Guoqiang Lan

A quantum computer is a type of computer that uses the principles of quantum mechanics to perform calculations. Quantum computers are still in their early stages of development, but TensorFlow provides a library called TensorFlow Quantum (TFQ) for hybrid quantum classical machine learning. One of the models in quantum computing is the Quantum Neural Network (QNN). QNNs are derived from classical neural networks and are designed to handle quantum bits (qubits) and facilitate the transmission of quantum circuits. Quantum Neural Networks (QNNs) are machine learning models designed to use the principles of quantum mechanics for tasks such as image classification. They can process classical image data, but classical data cannot directly achieve superposition states, which is a key feature of quantum systems. Thus, the data must be readable and able to be appropriately input into quantum devices. QNN uses supervised learning methods to predict image data. A quantum neural network (QNN) using supervised learning was developed to classify images of cats and dogs. This implementation utilized quantum circuit simulation within the Python 3.7 programming environment.