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- Paeckel, Sebastian, et al.
(författare)
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Time-evolution methods for matrix-product states
- 2019
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Ingår i: Annals of Physics. - : ACADEMIC PRESS INC ELSEVIER SCIENCE. - 0003-4916 .- 1096-035X. ; 411
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Tidskriftsartikel (refereegranskat)abstract
- Matrix-product states have become the de facto standard for the representation of one-dimensional quantum many body states. During the last few years, numerous new methods have been introduced to evaluate the time evolution of a matrix-product state. Here, we will review and summarize the recent work on this topic as applied to finite quantum systems. We will explain and compare the different methods available to construct a time-evolved matrix-product state, namely the time-evolving block decimation, the MPO W-I,W-II method, the global Krylov method, the local Krylov method and the one- and two-site time-dependent variational principle. We will also apply these methods to four different representative examples of current problem settings in condensed matter physics. (C) 2019 The Author(s). Published by Elsevier Inc.
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| 2. |
- Salvaneschi, Guido, et al.
(författare)
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Language-integrated privacy-aware distributed queries
- 2019
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Ingår i: Proceedings of the ACM on Programming Languages. - : Association for Computing Machinery (ACM). - 2475-1421. ; 3:OOPSLA, s. 167:1-167:30
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Tidskriftsartikel (refereegranskat)abstract
- Distributed query processing is an effective means for processing large amounts of data. To abstract from the technicalities of distributed systems, algorithms for operator placement automatically distribute sequential data queries over the available processing units. However, current algorithms for operator placement focus on performance and ignore privacy concerns that arise when handling sensitive data.We present a new methodology for privacy-aware operator placement that both prevents leakage of sensitive information and improves performance. Crucially, our approach is based on an information-flow type system for data queries to reason about the sensitivity of query subcomputations. Our solution unfolds in two phases. First, placement space reduction generates deployment candidates based on privacy constraints using a syntax-directed transformation driven by the information-flow type system. Second, constraint solving selects the best placement among the candidates based on a cost model that maximizes performance. We verify that our algorithm preserves the sequential behavior of queries and prevents leakage of sensitive data. We implemented the type system and placement algorithm for a new query language SecQL and demonstrate significant performance improvements in benchmarks.
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