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Browsing by Author "Chen, Mengsu"

Now showing 1 - 4 of 4
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    Exact diagonalization study of strongly correlated topological quantum states
    Chen, Mengsu (Virginia Tech, 2019-02-04)
    A rich variety of phases can exist in quantum systems. For example, the fractional quantum Hall states have persistent topological characteristics that derive from strong interaction. This thesis uses the exact diagonalization method to investigate quantum lattice models with strong interaction. Our research topics revolve around quantum phase transitions between novel phases. The goal is to find the best schemes for realizing these novel phases in experiments. We studied the fractional Chern insulator and its transition to uni-directional stripes of particles. In addition, we studied topological Mott insulators with spontaneous time-reversal symmetry breaking induced by interaction. We also studied emergent kinetics in one-dimensional lattices with spin-orbital coupling. The exact diagonalization method and its implementation for studying these systems can easily be applied to study other strongly correlated systems.
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    Hadoop Project for IDEAL in CS5604
    Cadena, Jose; Chen, Mengsu; Wen, Chengyuan (Virginia Tech, 2015-05-11)
    The Integrated Digital Event Archive and Library (IDEAL) system addresses the need for combining the best of digital library and archive technologies in support of stakeholders who are remembering and/or studying important events. It leverages and extends the capabilities of the Internet Archive to develop spontaneous event collections that can be permanently archived as well as searched and accessed. IDEAL connects the processing of tweets and web pages, combining informal and formal media to support building collections on chosen general or specific events. Integrated services include topic identification, categorization (building upon special ontologies being devised), clustering, and visualization of data, information, and context. The objective for the course is to build a state-of-the-art information retrieval system in support of the IDEAL project. Students were assigned to eight teams, each of which focused on a different part of the system to be built. These teams were Solr, Classification, Hadoop, Noise Reduction, LDA, Clustering, Social Networks, and NER. As the Hadoop team, our focus is on making the information retrieval system scalable to large datasets by taking advantage of the distributed computing capabilities of the Apache Hadoop framework. We design and put in place a general schema for storing and updating data stored in our Hadoop cluster. Throughout the project, we coordinate with other teams to help them make use of readily available machine learning software for Hadoop, and we also provide support for using MapReduce. We found that different teams were able to easily integrate their results in the design we developed and that uploading these results into a data store for communication with Solr can be done, in the best cases, in a few minutes. We conclude that Hadoop is an appropriate framework for the IDEAL project; however, we also recommend exploring the use of the Spark framework.
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    How Reliable is the Crowdsourced Knowledge of Security Implementation?
    Chen, Mengsu (Virginia Tech, 2018-12)
    The successful crowdsourcing model and gamification design of Stack Overflow (SO) Q&A platform have attracted many programmers to ask and answer technical questions, regardless of their level of expertise. Researchers have recently found evidence of security vulnerable code snippets being possibly copied from SO to production software. This inspired us to study how reliable is SO in providing secure coding suggestions. In this project, we automatically extracted answer posts related to Java security APIs from the entire SO site. Then based on the known misuses of these APIs, we manually labeled each extracted code snippets as secure or insecure. In total, we extracted 953 groups of code snippets in terms of their similarity detected by clone detection tools, which corresponds to 785 secure answer posts and 644 insecure answer posts. Compared with secure answers, counter-intuitively, insecure answers has higher view counts (36,508 vs. 18,713), higher score (14 vs. 5), more duplicates (3.8 vs. 3.0) on average. We also found that 34% of answers provided by the so-called trusted users who have administrative privileges are insecure. Our finding reveals that there are comparable numbers of secure and insecure answers. Users cannot rely on community feedback to differentiate secure answers from insecure answers either. Therefore, solutions need to be developed beyond the current mechanism of SO or on the utilization of SO in security-sensitive software development.
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    Validating quantum-classical programming models with tensor network simulations
    McCaskey, Alexander; Dumitrescu, Eugene; Chen, Mengsu; Lyakh, Dmitry; Humble, Travis (PLOS, 2018-12-10)
    The exploration of hybrid quantum-classical algorithms and programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards classical intractability, validation and benchmarking are critical to understanding the utility of the hybrid computational model. In this paper, we demonstrate a newly developed quantum circuit simulator based on tensor network theory that enables intermediate-scale verification and validation of hybrid quantum-classical computing frameworks and programming models. We present our tensor-network quantum virtual machine (TNQVM) simulator which stores a multi-qubit wavefunction in a compressed (factorized) form as a matrix product state, thus enabling single-node simulations of larger qubit registers, as compared to brute-force state-vector simulators. Our simulator is designed to be extensible in both the tensor network form and the classical hardware used to run the simulation (multicore, GPU, distributed). The extensibility of the TNQVM simulator with respect to the simulation hardware type is achieved via a pluggable interface for different numerical backends (e.g., ITensor and Exa-TENSOR numerical libraries). We demonstrate the utility of our TNQVM quantum circuit simulator through the verification of randomized quantum circuits and the variational quantum eigensolver algorithm, both expressed within the eXtreme-scale ACCelerator (XACC) programming model.