Quantum Hamiltonian Complexity

Constraint satisfaction problems are a central pillar of modern computational complexity theory. This survey provides an introduction to the rapidly growing field of Quantum Hamiltonian Complexity, which includes the study of quantum constraint satisfaction problems. Over the past decade and a half, this field has witnessed fundamental breakthroughs, ranging from the establishment of a "Quantum Cook-Levin Theorem" to deep insights into the structure of 1D low-temperature quantum systems via so-called area laws. Our aim here is to provide a computer science-oriented introduction to the subject in order to help bridge the language barrier between computer scientists and physicists in the field. As such, we include the following in this survey: (1) The motivations and history of the field, (2) a glossary of condensed matter physics terms explained in computer-science friendly language, (3) overviews of central ideas from condensed matter physics, such as indistinguishable particles, mean field theory, tensor networks, and area laws, and (4) brief expositions of selected computer science-based results in the area. For example, as part of the latter, we provide a novel information theoretic presentation of Bravyi's polynomial time algorithm for Quantum 2-SAT.Comment: v4: published version, 127 pages, introduction expanded to include brief introduction to quantum information, brief list of some recent developments added, minor changes throughou

ANONYMITY AND PERCEIVED NETWORK-STRUCTURE: A MODEL OF VIRTUAL COMMUNITY KNOWLEDGE INTENTIONS

This study investigates the underlying motivational factors with regard to the knowledge exchange intentions (intention to obtain and provide knowledge) in virtual community contexts. Perceived virtual network structure, namely, virtual network connectivity and virtual network closeness are suggested as the important antecedents of knowledge sharing intentions in the context of virtual knowledge exchange community. Anonymity, one of the unique characteristics of virtual community but controversial due to its multi-facet effects, is considered in a structural model as a factor having impacts on a virtual network structure

Verifying chemical reaction network implementations: A pathway decomposition approach

The emerging fields of genetic engineering, synthetic biology, DNA computing, DNA nanotechnology, and molecular programming herald the birth of a new information technology that acquires information by directly sensing molecules within a chemical environment, stores information in molecules such as DNA, RNA, and proteins, processes that information by means of chemical and biochemical transformations, and uses that information to direct the manipulation of matter at the nanometer scale. To scale up beyond current proof-of-principle demonstrations, new methods for managing the complexity of designed molecular systems will need to be developed. Here we focus on the challenge of verifying the correctness of molecular implementations of abstract chemical reaction networks, where operation in a well-mixed “soup” of molecules is stochastic, asynchronous, concurrent, and often involves multiple intermediate steps in the implementation, parallel pathways, and side reactions. This problem relates to the verification of Petri nets, but existing approaches are not sufficient for providing a single guarantee covering an infinite set of possible initial states (molecule counts) and an infinite state space potentially explored by the system given any initial state. We address these issues by formulating a new theory of pathway decomposition that provides an elegant formal basis for comparing chemical reaction network implementations, and we present an algorithm that computes this basis. Our theory naturally handles certain situations that commonly arise in molecular implementations, such as what we call “delayed choice,” that are not easily accommodated by other approaches. We further show how pathway decomposition can be combined with weak bisimulation to handle a wider class that includes most currently known enzyme-free DNA implementation techniques. We anticipate that our notion of logical equivalence between chemical reaction network implementations will be valuable for other molecular implementations such as biochemical enzyme systems, and perhaps even more broadly in concurrency theory

Evidence for a preformed Cooper pair model in the pseudogap spectra of a Ca10(Pt4As8)(Fe2As2)5 single crystal with a nodal superconducting gap

For high-Tc superconductors, clarifying the role and origin of the pseudogap is essential for understanding the pairing mechanism. Among the various models describing the pseudogap, the preformed Cooper pair model is a potential candidate. Therefore, we present experimental evidence for the preformed Cooper pair model by studying the pseudogap spectrum observed in the optical conductivity of a Ca10(Pt4As8)(Fe2As2)5 (Tc = 34.6 K) single crystal. We observed a clear pseudogap structure in the optical conductivity and observed its temperature dependence. In the superconducting (SC) state, one SC gap with a gap size of {\Delta} = 26 cm-1, a scattering rate of 1/{\tau} = 360 cm-1 and a low-frequency extra Drude component were observed. Spectral weight analysis revealed that the SC gap and pseudogap are formed from the same Drude band. This means that the pseudogap is a gap structure observed as a result of a continuous temperature evolution of the SC gap observed below Tc. This provides clear experimental evidence for the preformed Cooper pair model.Comment: 15 pages, 4 figure

Predictive Analytics Model for Power Consumption in Manufacturing

AbstractA Smart Manufacturing (SM) system should be capable of handling high volume data, processing high velocity data and manipulating high variety data. Big data analytics can enable timely and accurate insights using machine learning and predictive analytics to make better decisions. The objective of this paper is to present big data analytics modeling in the metal cutting industry. This paper includes: 1) identification of manufacturing data to be analyzed, 2) design of a functional architecture for deriving analytic models, and 3) design of an analytic model to predict a sustainability performance especially power consumption, using the big data infrastructure. A prototype system has been developed for this proof-of-concept, using open platform solutions including MapReduce, Hadoop Distributed File System (HDFS), and a machine-learning tool. To derive a cause-effect relationship of the analytic model, STEP-NC (a standard that enables the exchange of design- to-manufacturing data, especially machining) plan data and MTConnect machine monitoring data are used for a cause factor and an effect factor, respectively