Quantum Engineering

Let , where , is an odd prime, is a unit over , and In this article, we define a Gray map from to , we study the structure of skew --constacyclic codes over , and then we give the necessary and sufficient conditions for skew --constacyclic codes over to satisfy dual containing. Further, we have obtained some new nonbinary quantum codes from skew --constacyclic over by using the CSS construction.

Quantum machine learning uses quantum mechanical concepts of superposition of states to make the decision. In this work, we used these quantum advantages to enhance deep reinforcement learning (DRL). Our primary and foremost goal is to investigate and elucidate a way of representing and solving the frozen lake problems by using PennyLane which contains Xanadu’s back-end quantum processing unit. This paper specifically discusses how to enhance classical deep reinforcement learning algorithms with quantum computing technology, making quantum agents get a maximum reward after a fixed number of epochs and realizing the effect of a number of variational quantum layers on the trainability of enhanced framework. We have analyzed that, as the number of layers increases, the ability of the quantum agent to converge to the optimal state also increases. For this work, we have trained the framework agent with 2, 3, and 5 variational quantum layers. An agent with 2 layers converges to a total reward of 0.95 after the training episode of 526. The other agent with layers converges to a total reward of 0.95 after the training episode of 397 and the agent which uses 5 quantum variational layers converges to a total reward of 0.95 after the training episode of 72. From this, we can understand that the agent with a more variational layer exploits more and converges to the optimal state before the other agent. We also analyzed our work in terms of different learning rate hyperparameters. We recorded every single learning epoch to demonstrate the outcomes of enhanced DRL algorithms with selected 0.1, 0.2, 0.3, and 0.4 learning rates or alpha values. From this result, we can conclude that the greater the learning rate values in quantum deep reinforcement learning, the fewer timesteps it takes to move from the start point to the goal state.

In order to boost the security and confidentiality of information in quantum images, on the foundation of the NEQR model, a novel quantum watermarking scheme combining quantum Hilbert scrambling with steganography based on the Moiré fringe is designed in this paper. First of all, for carrier image, and watermark image, the color information and position information are denoted, respectively, by the NEQR model. Next, the watermark image is converted to a disordered image by quantum Hilbert scrambling, and the message of the original watermark image cannot be gained from the disordered image. At last, the watermark image after scrambling is embedded into the carrier image through the steganography of the Moiré fringe, obtaining the watermarked image. Due to the unitary image of the quantum gate, quantum Hilbert inverse scrambling is the opposite process of quantum Hilbert scrambling. In addition, the watermark image can be completely extracted from the watermarked image. What’s more, the experimental simulation and performance analysis of the scheme are done. The experimental simulation proves the feasibility of this algorithm. Visually, there is no difference between the carrier image and the watermarked image. The PSNR between the watermarked image and the carrier image is measured, which quantitatively shows the high similarity. In addition, the time complexity of the quantum circuit is lower than some other quantum image watermarking schemes, which proves the simplicity of this scheme.

In this paper, we propose a multiscale entanglement renormalization ansatz (MERA) feature extraction method based on a novel quantum convolutional neural network (QCNN) for binary scanning tunneling microscopy (STM) image classification. We design QCNN quantum circuits for state preparation, quantum convolution, and quantum pooling in the TensorFlow quantum framework and compare the performance of QCNN classifier and two hybrid quantum-classical QCNN models. Adversarial attacks are considered as a type of interpretable method to evaluate the robustness of QCNN models. The similarity between the pixels of image bitplane slicing and Ising phase transition opens up new ways for exploring classification performance enhancement by QCNN classifiers. Classification performance of different bitplanes of QCNN also shows that they can robustly resist adversarial attacks such as FGSM, CW, JSMA, and DEEPFOOL.

The power of quantum computing may bring a revolution in finance and banking. Here, we present quantum software, BQ-Bank for option pricing, Value at Risk, portfolio optimization, and others. BQ-Bank can be run on a real quantum computer such as superconducting system or on an emulation system based on a classical computer with an interface. BQ-Bank, such as other quantum types of software, represents a new generation of the toolbox that likely brings disruptive innovations to the financial industry and banking market in the future. BQ-Bank also provides the classical Monte Carlo solution, so that users can compare their quantum results with classical ones directly. Our simulation results for a variety of examples show the superiority of quantum solutions.

We describe a quantum chemistry simulation software program BQ-Chem, which can calculate the low-energy spectrum and potential energy surface of molecules on a quantum computer. BQ-Chem is based on the full quantum eigensolver (FQE), which is implemented with a quantum gradient descent algorithm. Benefiting from FQE, BQ-Chem can perform all the calculations on a quantum computer. Compared with the classical optimization methods which encounter the optimization difficulty of high-dimensional and multivariable functions in dealing with multielectron orbitals of macromolecules, FQE provides an exponential speedup. FQE works fully on a quantum computer; thus, BQ-Chem can be smoothly transited to future large-scale quantum computers.