4,727 research outputs found
Full-counting statistics of particle distribution on a digital quantum computer
Full-counting statistics (FCS) provides a powerful framework to access the
statistical information of a system from the characteristic function. However,
applications of FCS for generic interacting quantum systems often be hindered
by the intrinsic difficulty of classical simulation of quantum many-body
problems. Here, we propose a quantum algorithm for FCS that can obtain both the
particle distribution and cumulants of interacting systems. The algorithm
evaluates the characteristic functions by quantum computing and then extracts
the distribution and cumulants with classical post-processing. With digital
signal processing theory, we analyze the dependency of accuracy with the number
of sampling points for the characteristic functions. We show that the desired
number of sampling points for accurate FCS can be reduced by filtering some
components of the quantum state that are not of interest. By numeral
simulation, we demonstrate FCS of domain walls for the mixed Ising model. The
algorithm suggests an avenue for studying full-counting statistics on quantum
computers
Empirical research on the evaluation model and method of sustainability of the open source ecosystem
The development of open source brings new thinking and production modes to software engineering and computer science, and establishes a software development method and ecological environment in which groups participate. Regardless of investors, developers, participants, and managers, they are most concerned about whether the Open Source Ecosystem can be sustainable to ensure that the ecosystem they choose will serve users for a long time. Moreover, the most important quality of the software ecosystem is sustainability, and it is also a research area in Symmetry. Therefore, it is significant to assess the sustainability of the Open Source Ecosystem. However, the current measurement of the sustainability of the Open Source Ecosystem lacks universal measurement indicators, as well as a method and a model. Therefore, this paper constructs an Evaluation Indicators System, which consists of three levels: The target level, the guideline level and the evaluation level, and takes openness, stability, activity, and extensibility as measurement indicators. On this basis, a weight calculation method, based on information contribution values and a Sustainability Assessment Model, is proposed. The models and methods are used to analyze the factors affecting the sustainability of Stack Overflow (SO) ecosystem. Through the analysis, we find that every indicator in the SO ecosystem is partaking in different development trends. The development trend of a single indicator does not represent the sustainable development trend of the whole ecosystem. It is necessary to consider all of the indicators to judge that ecosystem’s sustainability. The research on the sustainability of the Open Source Ecosystem is helpful for judging software health, measuring development efficiency and adjusting organizational structure. It also provides a reference for researchers who study the sustainability of software engineering
SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR
curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese
Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved
a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the
predicted and the original JND distributions of only 0.072
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