Generating entangled states from coherent states in circuit-QED

Entangled states are self-evidently important to a wide range of applications in quantum communication and quantum information processing. We propose an efficient and convenient two-step protocol for generating Bell states and NOON states of two microwave resonators from merely coherent states. In particular, we derive an effective Hamiltonian for resonators coupled to a superconducting $\Lambda$-type qutrit in the dispersive regime. By the excitation-number-dependent Stark shifts of the qutrit transition frequencies, we are able to individually control the amplitudes of specified Fock states of the resonators associated with relevant qutrit transition, using carefully tailored microwave drive signals. Thereby an arbitrary bipartite entangled state in Fock space can be generated by a typical evolution-and-measurement procedure. We analysis the undesired state transitions and the robustness of our protocol against the systematic errors from the microwave driving intensity and frequency, the quantum decoherence of all components, and the crosstalk of two resonators. In addition, we demonstrate that our protocol can be extended to a similar scenario with a $\Xi$-type qutrit.Comment: 13 pages, 11 figures, 1 tabl

Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast

Geometry and color information provided by the point clouds are both crucial for 3D scene understanding. Two pieces of information characterize the different aspects of point clouds, but existing methods lack an elaborate design for the discrimination and relevance. Hence we explore a 3D self-supervised paradigm that can better utilize the relations of point cloud information. Specifically, we propose a universal 3D scene pre-training framework via Geometry-Color Contrast (Point-GCC), which aligns geometry and color information using a Siamese network. To take care of actual application tasks, we design (i) hierarchical supervision with point-level contrast and reconstruct and object-level contrast based on the novel deep clustering module to close the gap between pre-training and downstream tasks; (ii) architecture-agnostic backbone to adapt for various downstream models. Benefiting from the object-level representation associated with downstream tasks, Point-GCC can directly evaluate model performance and the result demonstrates the effectiveness of our methods. Transfer learning results on a wide range of tasks also show consistent improvements across all datasets. e.g., new state-of-the-art object detection results on SUN RGB-D and S3DIS datasets. Codes will be released at https://github.com/Asterisci/Point-GCC

Mixed leaf litter decomposition and N, P release with a focus on Phyllostachys edulis (Carrière) J. Houz. forest in subtropical southeastern China

As an important non-wood forest product and wood substitute, Moso bamboo grows extremely rapidly and hence acquires large quantities of nutrients from the soil. With regard to litter decomposition, N and P release in Moso bamboo forests is undoubtedly important; however, to date, no comprehensive analysis has been conducted. Here, we chose two dominant species (i.e., Cunninghamia lanceolata and Phoebe bournei), in addition to Moso bamboo, which are widely distributed in subtropical southeastern China, and created five leaf litter mixtures (PE100, PE80PB20, PE80CL20, PE50PB50 and PE50CL50) to investigate species effects on leaf litter decomposition and nutrient release (N and P) via the litterbag method. Over a one-year incubation experiment, mass loss varied significantly with litter type (P 0.94, P < 0.001). N and P had different patterns of release; overall, N showed great temporal variation, while P was released from the litter continually. The mixture of Moso bamboo and Phoebe bournei (PE80PB20 and PE50PB50) showed significantly faster P release compared to the other three types, but there was no significant difference in N release. Litter decomposition and P release were related to initial litter C/N ratio, C/P ratio, and/or C content, while no significant relationship between N release and initial stoichiometric ratios was found. The Moso bamboo–Phoebe bournei (i.e., bamboo–broadleaved) mixture appeared to be the best choice for nutrient return and thus productivity and maintenance of Moso bamboo in this region

Huber Kalman Filter for Wi-Fi based Vehicle Driver\u27s Respiration Detection

The use of breath detection in vehicles can reduce the number of vehicular accidents caused by drivers in poor physical condition. Prior studies of contactless respiration detection mainly targeted a static person. However, there are emerging applications to sense a driver, with emphasis on contactless methods. For example, being able to detect a driver\u27s respiration while driving by using a vehicular Wi-Fi system can significantly enhance driving safety. The sensing system can be mounted on the back of the driver\u27s seat, and it can sense the tiny chest displacement of the driver via Wi-Fi signals. The body displacement and car vibrations could introduce significant noise in the sensed signal. The noise then needs to be filtered to obtain the driver\u27s respiration. In this work, the noise in the sensed signal is proposed to be reduced using a Huber Kalman filter to restore the original respiration curve. Through several experiments in terms of different drivers, different car models, multiple passengers, and abnormal breathing, we demonstrate the accuracy and robustness of the Huber Kalman filter in driver\u27s respiration

Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization

Emergent hardwares can support mixed precision CNN models inference that assign different bitwidths for different layers. Learning to find an optimal mixed precision model that can preserve accuracy and satisfy the specific constraints on model size and computation is extremely challenge due to the difficult in training a mixed precision model and the huge space of all possible bit quantizations. In this paper, we propose a novel soft Barrier Penalty based NAS (BP-NAS) for mixed precision quantization, which ensures all the searched models are inside the valid domain defined by the complexity constraint, thus could return an optimal model under the given constraint by conducting search only one time. The proposed soft Barrier Penalty is differentiable and can impose very large losses to those models outside the valid domain while almost no punishment for models inside the valid domain, thus constraining the search only in the feasible domain. In addition, a differentiable Prob-1 regularizer is proposed to ensure learning with NAS is reasonable. A distribution reshaping training strategy is also used to make training more stable. BP-NAS sets new state of the arts on both classification (Cifar-10, ImageNet) and detection (COCO), surpassing all the efficient mixed precision methods designed manually and automatically. Particularly, BP-NAS achieves higher mAP (up to 2.7\% mAP improvement) together with lower bit computation cost compared with the existing best mixed precision model on COCO detection.Comment: ECCV202