Concentration of Data Encoding in Parameterized Quantum Circuits

Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term quantum advantages in meaningful tasks, including machine learning and combinatorial optimization. When applied to tasks involving classical data, such algorithms generally begin with quantum circuits for data encoding and then train quantum neural networks (QNNs) to minimize target functions. Although QNNs have been widely studied to improve these algorithms' performance on practical tasks, there is a gap in systematically understanding the influence of data encoding on the eventual performance. In this paper, we make progress in filling this gap by considering the common data encoding strategies based on parameterized quantum circuits. We prove that, under reasonable assumptions, the distance between the average encoded state and the maximally mixed state could be explicitly upper-bounded with respect to the width and depth of the encoding circuit. This result in particular implies that the average encoded state will concentrate on the maximally mixed state at an exponential speed on depth. Such concentration seriously limits the capabilities of quantum classifiers, and strictly restricts the distinguishability of encoded states from a quantum information perspective. We further support our findings by numerically verifying these results on both synthetic and public data sets. Our results highlight the significance of quantum data encoding in machine learning tasks and may shed light on future encoding strategies.Comment: 26 pages including appendi

Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey

Since signet ring cells (SRCs) are associated with high peripheral metastasis rate and dismal survival, they play an important role in determining surgical approaches and prognosis, while they are easily missed by even experienced pathologists. Although automatic diagnosis SRCs based on deep learning has received increasing attention to assist pathologists in improving the diagnostic efficiency and accuracy, the existing works have not been systematically overviewed, which hindered the evaluation of the gap between algorithms and clinical applications. In this paper, we provide a survey on SRC analysis driven by deep learning from 2008 to August 2023. Specifically, the biological characteristics of SRCs and the challenges of automatic identification are systemically summarized. Then, the representative algorithms are analyzed and compared via dividing them into classification, detection, and segmentation. Finally, for comprehensive consideration to the performance of existing methods and the requirements for clinical assistance, we discuss the open issues and future trends of SRC analysis. The retrospect research will help researchers in the related fields, particularly for who without medical science background not only to clearly find the outline of SRC analysis, but also gain the prospect of intelligent diagnosis, resulting in accelerating the practice and application of intelligent algorithms

Additive Manufacturing of Sn63Pb37 Component by Micro-coating

AbstractMicro-coating is a novel technology to build near-net component layer by layer, which uses a crucible and nozzle instead of a weld head and wire feeder to supply material compared with shaped metal deposition. A pneumatic system is adopted to adjust liquid metal flow rate and the layer height is controlled by the distance between nozzle and substrate. Height and width of a single channel are measured by confocal microscopy, it is found that the error between numerical results and experiment are 5.5% and 1.1%. Tensile stress vertically to the deposition layers reaches to 40.89Mpa, while tensile stress parallel to the deposition layers gives a value of 43.14Mpa. Yield stress of vertically and parallel to the layer are respectively 34.28Mpa and 35.23Mpa. Specimens exhibit better mechanical properties than casting component, whose tensile stress and yield stress are respectively 36.51Mpa and 29.25Mpa

An Improved Histogram Based Boosting Detection Rate Video Watermarking Algorithm

The existing histogram based video watermarking algorithm with temporal modulated is robust to combined attacks, but the watermark detection rate is not high due to watermark cannot embedded to the smoothness and still areas effectively. To increase the watermark detection rate, in this paper, we proposed the improved algorithm of shot segmentation first and then propose an improved video watermarking algorithm which firstly construct the watermark template in each frame video in the same shot through computing block based histogram and selecting the position of the relative high variance. Then we embed the watermark template into the video frame by temporal modulation without changing the destination of the shot group of the consecutive frames. The watermark sequence is extracted by comparing the correlation distribution of video frame and corresponding watermark template in the time domain. Experimental results demonstrate that the proposed algorithm is robust to recording attacks and guarantee the watermarking video quality at the same time, besides the watermark sequences can embedded to the smoothness and still areas effectively, and the watermark detection rate can increase by about 10% than previous methods

Dynamic state of ecosystem carrying capacity under island urbanization: a case study of Pingtan Island in the Southeastern coast of China

The assessment of ecological environment during the large-scale development of islands is a major topic in the study of current coastal islands. Choosing the appropriate assessment method to evaluate the suitability of carrying capacity of islands and making relevant suggestions are significant to the sustainable development of islands. Ecological footprint method is used to analyze the ecological carrying capacity of Pingtan Island (PI) from 2005 to 2016 for promoting the coordinated rational development and construction and ecological environment of the island. Although PI is in rapid urban development and construction, the island maintains secure and stable ecological conditions. PI is used as a research case to analyze the sustainable development of the ecological environment through the carrying capacity of the island ecosystem

Neuroinflammation Induced by Surgery Does Not Impair the Reference Memory of Young Adult Mice

Postoperative cognitive dysfunction (POCD) increases morbidity and mortality after surgery. But the underlying mechanism is not clear yet. While age is now accepted as the top one risk factor for POCD, results from studies investigating postoperative cognitive functions in adults have been controversial, and data about the very young adult individuals are lacking. The present study investigated the spatial reference memory, IL-1 , IL-6, and microglia activation changes in the hippocampus in 2-month-old mice after anesthesia and surgery. We found that hippocampal IL-1 and IL-6 increased at 6 hours after surgery. Microglia were profoundly activated in the hippocampus 6 to 24 hours after surgery. However, no significant behavior changes were found in these mice. These results indicate that although anesthesia and surgery led to neuroinflammation, the latter was insufficient to impair the spatial reference memory of young adult mice