4 Search Results for "Shen, Yu-Ching"


Document
On the Impossibility of General Parallel Fast-Forwarding of Hamiltonian Simulation

Authors: Nai-Hui Chia, Kai-Min Chung, Yao-Ching Hsieh, Han-Hsuan Lin, Yao-Ting Lin, and Yu-Ching Shen

Published in: LIPIcs, Volume 264, 38th Computational Complexity Conference (CCC 2023)


Abstract
Hamiltonian simulation is one of the most important problems in the field of quantum computing. There have been extended efforts on designing algorithms for faster simulation, and the evolution time T for the simulation greatly affect algorithm runtime as expected. While there are some specific types of Hamiltonians that can be fast-forwarded, i.e., simulated within time o(T), for some large classes of Hamiltonians (e.g., all local/sparse Hamiltonians), existing simulation algorithms require running time at least linear in the evolution time T. On the other hand, while there exist lower bounds of Ω(T) circuit size for some large classes of Hamiltonian, these lower bounds do not rule out the possibilities of Hamiltonian simulation with large but "low-depth" circuits by running things in parallel. As a result, physical systems with system size scaling with T can potentially do a fast-forwarding simulation. Therefore, it is intriguing whether we can achieve fast Hamiltonian simulation with the power of parallelism. In this work, we give a negative result for the above open problem in various settings. In the oracle model, we prove that there are time-independent sparse Hamiltonians that cannot be simulated via an oracle circuit of depth o(T). In the plain model, relying on the random oracle heuristic, we show that there exist time-independent local Hamiltonians and time-dependent geometrically local Hamiltonians on n qubits that cannot be simulated via an oracle circuit of depth o(T/n^c), where the Hamiltonians act on n qubits, and c is a constant. Lastly, we generalize the above results and show that any simulators that are geometrically local Hamiltonians cannot do the simulation much faster than parallel quantum algorithms.

Cite as

Nai-Hui Chia, Kai-Min Chung, Yao-Ching Hsieh, Han-Hsuan Lin, Yao-Ting Lin, and Yu-Ching Shen. On the Impossibility of General Parallel Fast-Forwarding of Hamiltonian Simulation. In 38th Computational Complexity Conference (CCC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 264, pp. 33:1-33:45, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)


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@InProceedings{chia_et_al:LIPIcs.CCC.2023.33,
  author =	{Chia, Nai-Hui and Chung, Kai-Min and Hsieh, Yao-Ching and Lin, Han-Hsuan and Lin, Yao-Ting and Shen, Yu-Ching},
  title =	{{On the Impossibility of General Parallel Fast-Forwarding of Hamiltonian Simulation}},
  booktitle =	{38th Computational Complexity Conference (CCC 2023)},
  pages =	{33:1--33:45},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-282-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{264},
  editor =	{Ta-Shma, Amnon},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2023.33},
  URN =		{urn:nbn:de:0030-drops-183038},
  doi =		{10.4230/LIPIcs.CCC.2023.33},
  annote =	{Keywords: Hamiltonian simulation, Depth lower bound, Parallel query lower bound}
}
Document
Topological Data Analysis Reveals Principles of Chromosome Structure in Cellular Differentiation

Authors: Natalie Sauerwald, Yihang Shen, and Carl Kingsford

Published in: LIPIcs, Volume 143, 19th International Workshop on Algorithms in Bioinformatics (WABI 2019)


Abstract
Topological data analysis (TDA) is a mathematically well-founded set of methods to derive robust information about the structure and topology of data. It has been applied successfully in several biological contexts. Derived primarily from algebraic topology, TDA rigorously identifies persistent features in complex data, making it well-suited to better understand the key features of three-dimensional chromosome structure. Chromosome structure has a significant influence in many diverse genomic processes and has recently been shown to relate to cellular differentiation. While there exist many methods to study specific substructures of chromosomes, we are still missing a global view of all geometric features of chromosomes. By applying TDA to the study of chromosome structure through differentiation across three cell lines, we provide insight into principles of chromosome folding and looping. We identify persistent connected components and one-dimensional topological features of chromosomes and characterize them across cell types and stages of differentiation. Availability: Scripts to reproduce the results from this study can be found at https://github.com/Kingsford-Group/hictda

Cite as

Natalie Sauerwald, Yihang Shen, and Carl Kingsford. Topological Data Analysis Reveals Principles of Chromosome Structure in Cellular Differentiation. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 23:1-23:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{sauerwald_et_al:LIPIcs.WABI.2019.23,
  author =	{Sauerwald, Natalie and Shen, Yihang and Kingsford, Carl},
  title =	{{Topological Data Analysis Reveals Principles of Chromosome Structure in Cellular Differentiation}},
  booktitle =	{19th International Workshop on Algorithms in Bioinformatics (WABI 2019)},
  pages =	{23:1--23:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-123-8},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{143},
  editor =	{Huber, Katharina T. and Gusfield, Dan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2019.23},
  URN =		{urn:nbn:de:0030-drops-110537},
  doi =		{10.4230/LIPIcs.WABI.2019.23},
  annote =	{Keywords: topological data analysis, chromosome structure, Hi-C, topologically associating domains}
}
Document
Random Noise Increases Kolmogorov Complexity and Hausdorff Dimension

Authors: Gleb Posobin and Alexander Shen

Published in: LIPIcs, Volume 126, 36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019)


Abstract
Consider a bit string x of length n and Kolmogorov complexity alpha n (for some alpha<1). It is always possible to increase the complexity of x by changing a small fraction of bits in x [Harry Buhrman et al., 2005]. What happens with the complexity of x when we randomly change each bit independently with some probability tau? We prove that a linear increase in complexity happens with high probability, but this increase is smaller than in the case of arbitrary change considered in [Harry Buhrman et al., 2005]. The amount of the increase depends on x (strings of the same complexity could behave differently). We give exact lower and upper bounds for this increase (with o(n) precision). The same technique is used to prove the results about the (effective Hausdorff) dimension of infinite sequences. We show that random change increases the dimension with probability 1, and provide an optimal lower bound for the dimension of the changed sequence. We also improve a result from [Noam Greenberg et al., 2018] and show that for every sequence omega of dimension alpha there exists a strongly alpha-random sequence omega' such that the Besicovitch distance between omega and omega' is 0. The proofs use the combinatorial and probabilistic reformulations of complexity statements and the technique that goes back to Ahlswede, Gács and Körner [Ahlswede et al., 1976].

Cite as

Gleb Posobin and Alexander Shen. Random Noise Increases Kolmogorov Complexity and Hausdorff Dimension. In 36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 126, pp. 57:1-57:14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)


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@InProceedings{posobin_et_al:LIPIcs.STACS.2019.57,
  author =	{Posobin, Gleb and Shen, Alexander},
  title =	{{Random Noise Increases Kolmogorov Complexity and Hausdorff Dimension}},
  booktitle =	{36th International Symposium on Theoretical Aspects of Computer Science (STACS 2019)},
  pages =	{57:1--57:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-100-9},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{126},
  editor =	{Niedermeier, Rolf and Paul, Christophe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2019.57},
  URN =		{urn:nbn:de:0030-drops-102969},
  doi =		{10.4230/LIPIcs.STACS.2019.57},
  annote =	{Keywords: Kolmogorov complexity, effective Hausdorff dimension, random noise}
}
Document
PerfBlower: Quickly Detecting Memory-Related Performance Problems via Amplification

Authors: Lu Fang, Liang Dou, and Guoqing Xu

Published in: LIPIcs, Volume 37, 29th European Conference on Object-Oriented Programming (ECOOP 2015)


Abstract
Performance problems in managed languages are extremely difficult to find. Despite many efforts to find those problems, most existing work focuses on how to debug a user-provided test execution in which performance problems already manifest. It remains largely unknown how to effectively find performance bugs before software release. As a result, performance bugs often escape to production runs, hurting software reliability and user experience. This paper describes PerfBlower, a general performance testing framework that allows developers to quickly test Java programs to find memory-related performance problems. PerfBlower provides (1) a novel specification language ISL to describe a general class of performance problems that have observable symptoms; (2) an automated test oracle via \emph{virtual amplification}; and (3) precise reference-path-based diagnostic information via object mirroring. Using this framework, we have amplified three different types of problems. Our experimental results demonstrate that (1) ISL is expressive enough to describe various memory-related performance problems; (2) PerfBlower successfully distinguishes executions with and without problems; 8 unknown problems are quickly discovered under small workloads; and (3) PerfBlower outperforms existing detectors and does not miss any bugs studied before in the literature.

Cite as

Lu Fang, Liang Dou, and Guoqing Xu. PerfBlower: Quickly Detecting Memory-Related Performance Problems via Amplification. In 29th European Conference on Object-Oriented Programming (ECOOP 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 37, pp. 296-320, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)


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@InProceedings{fang_et_al:LIPIcs.ECOOP.2015.296,
  author =	{Fang, Lu and Dou, Liang and Xu, Guoqing},
  title =	{{PerfBlower: Quickly Detecting Memory-Related Performance Problems via Amplification}},
  booktitle =	{29th European Conference on Object-Oriented Programming (ECOOP 2015)},
  pages =	{296--320},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-86-6},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{37},
  editor =	{Boyland, John Tang},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2015.296},
  URN =		{urn:nbn:de:0030-drops-52278},
  doi =		{10.4230/LIPIcs.ECOOP.2015.296},
  annote =	{Keywords: Performance bugs, memory problems, managed languages, garbage collection\}}
}
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