Non-adiabatic holonomic quantum computation in linear system-bath coupling

Non-adiabatic holonomic quantum computation in decoherence-free subspaces protects quantum information from control imprecisions and decoherence. For the non-collective decoherence that each qubit has its own bath, we show the implementations of two non-commutable holonomic single-qubit gates and one holonomic nontrivial two-qubit gate that compose a universal set of non-adiabatic holonomic quantum gates in decoherence-free-subspaces of the decoupling group, with an encoding rate of $\frac{N-2}{N}$. The proposed scheme is robust against control imprecisions and the non-collective decoherence, and its non-adiabatic property ensures less operation time. We demonstrate that our proposed scheme can be realized by utilizing only two-qubit interactions rather than many-qubit interactions. Our results reduce the complexity of practical implementation of holonomic quantum computation in experiments. We also discuss the physical implementation of our scheme in coupled microcavities.Comment: 2 figures; accepted by Sci. Re

Dynamics of a Stage Structured Pest Control Model in a Polluted Environment with Pulse Pollution Input

By using pollution model and impulsive delay differential equation, we formulate a pest control model with stage structure for natural enemy in a polluted environment by introducing a constant periodic pollutant input and killing pest at different fixed moments and investigate the dynamics of such a system. We assume only that the natural enemies are affected by pollution, and we choose the method to kill the pest without harming natural enemies. Sufficient conditions for global attractivity of the natural enemy-extinction periodic solution and permanence of the system are obtained. Numerical simulations are presented to confirm our theoretical results

Characterization of a sensitive biosensor based on an unmodified DNA and gold nanoparticle composite and its application in diquat determination

AbstractDNA usually adsorbs gold nanoparticles by virtue of mercapto or amino groups at one end of a DNA molecule. However, in this paper, we report a sensitive biosensor constructed using unmodified DNA molecules with consecutive adenines (CA DNA) and gold nanoparticles (GNPs). The CA DNA–GNP composite was fabricated on gold electrodes and characterized by using of scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS) and the electrochemical method. Using an electrochemical quartz crystal microbalance (EQCM), the mechanism by which the CA DNA and GNPs combined was also studied. The modified electrode exhibited an ultrasensitive response to diquat. Differential pulse voltammetry (DPV) was used to study the linear relationships between concentrations and reduction peak currents, ranging from 1.0×10−9M to 1.2×10−6M. The detection limit of it is 2.0×10−10M. The feasibility of the proposed assay for use in human urine and grain was investigated, and the satisfactory results were obtained

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregard frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.Comment: 13 pages, 7 figures, submitted to Biomedical Optics Express special issu