The concept of non‐Hermitian systems, also known as exceptional points (EPs), has recently emerged as a novel approach for designing the response of systems that exchange energy with the external environment. The system behaves as a non‐Hermitian Hamiltonian (NH Hamiltonian), the real part of which is related to the internal dynamics of the system, and the imaginary part reflects the energy exchange between the system and the outside environment. By utilizing double EIT windows and adjusting the intensity of the dressing field, the number and positions of eigenvalues can be modulated to construct quantum entanglement sources for non‐Hermitian systems, thereby laying the foundation for quantum communication and quantum computing. To prepare photon entanglement sources, it is necessary to study the correlation of high‐dimensional quantized energy levels under PT symmetry and symmetry breaking induced by the dressing field; Hamiltonian correlation matrix of the non‐Hermitian system characterizes the relationship between gain and loss, detuning and dephasing rate in the reaction energy‐level system, and the coupling relationship of the dressing field. The energy‐level system itself is non‐Hermitian, and the study of symmetry breaking is conducted by controlling EPs during the level control process through the dressing field, thus eliminating the need for introducing artificial photonic structures. The third‐order non‐Hermitian characteristics of the PT‐NH system reflect the positional relationship between the cascade field and the nested field. When the Rabi frequency (dephase rate) of the dressing field is the dominant factor, the real (imaginary) part of the eigenvalues is split, resulting in in‐phase (out‐of‐phase) constructive (destructive) transformations of the dressing order.

Unauthorized access to hazardous substances poses a serious threat to public health. Typically, these substances, such as chemicals, are housed within warehouses or laboratory facilities, making strict control over any unauthorized entry. The control can be achieved through various measures, including access control systems, registration systems, remote inspection and surveillance, and personnel checks. Given its essential significance, the security of surveillance system data transmission plays a critical role. On the one hand, authorized entities must be capable of detecting any malicious data interception, and on the other hand, confidential information must remain safeguarded when an eavesdropper is identified. To address transmission security, we have developed a quantum-communication-based system for transmitting surveillance imagery of the hazardous chemical storage area entrance. This system effectively thwarts eavesdropping and data tampering during transmission, thereby enhancing the security of conventional monitoring systems.

Corrosion of mild steel has gained a lot of attention by many industries and academia. This study was aimed to find the corrosion inhibition properties of hydrazine and its 11 derived molecules for mild steel using computational method, namely, the density functional theory with Beck 3‐parameter Lee Yang and Parr functional along with the 6‐311++G (d, p) basis set using simple quantum chemical parameters. The main quantum chemical parameters were energies of highest occupied and lowest unoccupied molecular orbitals and their energy gap, dipole moment, hardness, softness, electrophilicity index, the fractions of electrons transmitted, the change energy back‐donation, electron‐donating power, electron‐accepting power, molecular electrostatic potential, and Fukui function as well as polarizability. The results indicated that hydrazine derivatives have good anticorrosion nature which is consistent with the experimental findings. Out of the 12 target inhibitor molecules, N′‐(3‐nitrobenzylidene) hydrazine carbodithioic acid (NBHCA) and N′‐(4‐dimethylaminobenzylidene) hydrazine carbodithioic acid (DABHCA) are the most effective inhibitors. According to the value of the quantum chemical parameters, EHOMO, ΔEgap, Χ, η, σ, ε, ΔN, α, and ΔEb-d DABHCA showed the top effective anticorrosion inhibitor, and by the value of the quantum chemical parameters, ELUMO, TE, ω−, and ω+ NBHCA showed an effective anticorrosion inhibitor. The quantum chemical parameter data showed that NBHCA and DABHCA are nearly equivalent in their values, which is consistent with experimental values. Furthermore, a multiple‐linear regression equation was proposed as a quantitative structural activity relationship model to relate the calculated molecular descriptors of hydrazine derivatives to their experimental inhibition efficiencies obtained from literature. As a result, this model showed an excellent accuracy with an R‐squared of 0.924 and this uses to predict inhibition efficiencies of another molecules.

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.

Quantum Engineering is a peer-reviewed, open access journal that publishes research on the engineering of quantum information. It bridges the gap between engineers and scientists, enabling them to take advantage of new quantum breakthroughs.

The quantum network is an indispensable step toward multiuser and wide-area information interchange in the course of the development of quantum technology. When designing and deploying a quantum network, availability, robustness, flexibility, and expenditure need to be considered in a balanced way. In this article, we propose a network connected through optical switches, QNUS, that requires only terminals in the number of nodes in a quantum network, which is a great saving of resources.

Solving for quantum error correction remains one of the key challenges of quantum computing. Traditional decoding methods are limited by computing power and data scale, which restrict the decoding efficiency of color codes. There are many decoding methods that have been suggested to solve this problem. Machine learning is considered one of the most suitable solutions for decoding task of color code. We project the color code onto the surface code, use the deep Q network to iteratively train the decoding process of the color code and obtain the relationship between the inversion error rate and the logical error rate of the trained model and the performance of error correction. Our results show that through unsupervised learning, when iterative training is at least 300 times, a self-trained model can improve the error correction accuracy to 96.5%, and the error correction speed is about 13.8% higher than that of the traditional algorithm. We numerically show that our decoding method can achieve a fast prediction speed after training and a better error correction threshold.

The variational quantum eigensolver (VQE) is a promising algorithm to demonstrate quantum advantage on near-term noisy-intermediate-scale quantum (NISQ) computers. One central problem of VQE is the effect of noise, especially physical noise, on realistic quantum computers. We systematically study the effect of noise for the VQE algorithm by performing numerical simulations with various local noise models, including amplitude damping, dephasing, and depolarizing noise. We show that the ground state energy will deviate from the exact value as the noise probability increase, and typically, the noise will accumulate as the circuit depth increase. The results suggest that the noisy quantum system can remain entanglement at the noise level of NISQ devices by comparing the VQE solution with the mean-field solution for the many-body ground state problem. We build a noise model to capture the noise in a real quantum computer, and the corresponding numerical simulation is consistent with experimental results on IBM Quantum computers through cloud. Our work sheds new light on the practical research of noisy VQE, and the deep understanding of the noise effect of VQE will also help develop error mitigation techniques on near-term quantum computers.