The Experts below are selected from a list of 3570339 Experts worldwide ranked by ideXlab platform

Barry C Sanders - One of the best experts on this subject based on the ideXlab platform.

  • experimental quantum switching for exponentially superior quantum communication complexity
    Physical Review Letters, 2019
    Co-Authors: Kejin Wei, Nora Tischler, Siran Zhao, Juan Miguel Arrazola, Yang Liu, Weijun Zhang, Lixing You, Zhen Wang, Yuao Chen, Barry C Sanders
    Abstract:

    Finding exponential separation between quantum and classical Information tasks is like striking gold in quantum Information Research. Such an advantage is believed to hold for quantum computing but is proven for quantum communication complexity. Recently, a novel quantum resource called the quantum switch-which creates a coherent superposition of the causal order of events, known as quantum causality-has been harnessed theoretically in a new protocol providing provable exponential separation. We experimentally demonstrate such an advantage by realizing a superposition of communication directions for a two-party distributed computation. Our photonic demonstration employs d-dimensional quantum systems, qudits, up to d=2^{13} dimensions and demonstrates a communication complexity advantage, requiring less than (0.696±0.006) times the communication of any causally ordered protocol. These results elucidate the crucial role of the coherence of communication direction in achieving the exponential separation for the one-way processing task, and open a new path for experimentally exploring the fundamentals and applications of advanced features of indefinite causal structures.

  • experimental quantum switching for exponentially superior quantum communication complexity
    Physical Review Letters, 2019
    Co-Authors: Kejin Wei, Nora Tischler, Siran Zhao, Juan Miguel Arrazola, Yang Liu, Weijun Zhang, Lixing You, Zhen Wang, Yuao Chen, Barry C Sanders
    Abstract:

    Finding exponential separation between quantum and classical Information tasks is like striking gold in quantum Information Research. Such an advantage is believed to hold for quantum computing but is proven for quantum communication complexity. Recently, a novel quantum resource called the quantum switch---which creates a coherent superposition of the causal order of events, known as quantum causality---has been harnessed theoretically in a new protocol providing provable exponential separation. We experimentally demonstrate such an advantage by realizing a superposition of communication directions for a two-party distributed computation. Our photonic demonstration employs $d$-dimensional quantum systems, qudits, up to $d={2}^{13}$ dimensions and demonstrates a communication complexity advantage, requiring less than $(0.696\ifmmode\pm\else\textpm\fi{}0.006)$ times the communication of any causally ordered protocol. These results elucidate the crucial role of the coherence of communication direction in achieving the exponential separation for the one-way processing task, and open a new path for experimentally exploring the fundamentals and applications of advanced features of indefinite causal structures.

Bin Wang - One of the best experts on this subject based on the ideXlab platform.

  • an improved stochastic fractal search algorithm for 3d protein structure prediction
    Journal of Molecular Modeling, 2018
    Co-Authors: Changjun Zhou, Chuan Sun, Bin Wang, Xiaojun Wang
    Abstract:

    Protein structure prediction (PSP) is a significant area for biological Information Research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback Information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

  • stochastic fractal search algorithm for 3d protein structure prediction
    DEStech Transactions on Computer Science and Engineering, 2017
    Co-Authors: Chuan Sun, Ziqi Wei, Changjun Zhou, Bin Wang
    Abstract:

    Protein structure prediction (PSP) is a process to predict protein three-dimensional structures based on the known amino acid sequence, which has a very important significance for biological Information Research, disease treatment and drug Research and development and so on. PSP is a typical NP problem: firstly, we should establish the appropriate mathematical model according to the problem; secondly, a solution process for the model was given based on an appropriate optimization algorithm. In this paper, an original protein structure prediction scheme is transformed into a numerical optimization problem by adopting AB off-lattice model. Then the minimum free energy value of protein structure will be gained by using Stochastic Fractal Search algorithm (SFS). Experiments show that the proposed method outperforms other algorithms on the accuracy of calculating the protein sequence energy value, which is turned out to be an effective way to analyze protein structure.

Sarah Canino - One of the best experts on this subject based on the ideXlab platform.

Chuan Sun - One of the best experts on this subject based on the ideXlab platform.

  • an improved stochastic fractal search algorithm for 3d protein structure prediction
    Journal of Molecular Modeling, 2018
    Co-Authors: Changjun Zhou, Chuan Sun, Bin Wang, Xiaojun Wang
    Abstract:

    Protein structure prediction (PSP) is a significant area for biological Information Research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback Information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

  • stochastic fractal search algorithm for 3d protein structure prediction
    DEStech Transactions on Computer Science and Engineering, 2017
    Co-Authors: Chuan Sun, Ziqi Wei, Changjun Zhou, Bin Wang
    Abstract:

    Protein structure prediction (PSP) is a process to predict protein three-dimensional structures based on the known amino acid sequence, which has a very important significance for biological Information Research, disease treatment and drug Research and development and so on. PSP is a typical NP problem: firstly, we should establish the appropriate mathematical model according to the problem; secondly, a solution process for the model was given based on an appropriate optimization algorithm. In this paper, an original protein structure prediction scheme is transformed into a numerical optimization problem by adopting AB off-lattice model. Then the minimum free energy value of protein structure will be gained by using Stochastic Fractal Search algorithm (SFS). Experiments show that the proposed method outperforms other algorithms on the accuracy of calculating the protein sequence energy value, which is turned out to be an effective way to analyze protein structure.

Kejin Wei - One of the best experts on this subject based on the ideXlab platform.

  • experimental quantum switching for exponentially superior quantum communication complexity
    Physical Review Letters, 2019
    Co-Authors: Kejin Wei, Nora Tischler, Siran Zhao, Juan Miguel Arrazola, Yang Liu, Weijun Zhang, Lixing You, Zhen Wang, Yuao Chen, Barry C Sanders
    Abstract:

    Finding exponential separation between quantum and classical Information tasks is like striking gold in quantum Information Research. Such an advantage is believed to hold for quantum computing but is proven for quantum communication complexity. Recently, a novel quantum resource called the quantum switch-which creates a coherent superposition of the causal order of events, known as quantum causality-has been harnessed theoretically in a new protocol providing provable exponential separation. We experimentally demonstrate such an advantage by realizing a superposition of communication directions for a two-party distributed computation. Our photonic demonstration employs d-dimensional quantum systems, qudits, up to d=2^{13} dimensions and demonstrates a communication complexity advantage, requiring less than (0.696±0.006) times the communication of any causally ordered protocol. These results elucidate the crucial role of the coherence of communication direction in achieving the exponential separation for the one-way processing task, and open a new path for experimentally exploring the fundamentals and applications of advanced features of indefinite causal structures.

  • experimental quantum switching for exponentially superior quantum communication complexity
    Physical Review Letters, 2019
    Co-Authors: Kejin Wei, Nora Tischler, Siran Zhao, Juan Miguel Arrazola, Yang Liu, Weijun Zhang, Lixing You, Zhen Wang, Yuao Chen, Barry C Sanders
    Abstract:

    Finding exponential separation between quantum and classical Information tasks is like striking gold in quantum Information Research. Such an advantage is believed to hold for quantum computing but is proven for quantum communication complexity. Recently, a novel quantum resource called the quantum switch---which creates a coherent superposition of the causal order of events, known as quantum causality---has been harnessed theoretically in a new protocol providing provable exponential separation. We experimentally demonstrate such an advantage by realizing a superposition of communication directions for a two-party distributed computation. Our photonic demonstration employs $d$-dimensional quantum systems, qudits, up to $d={2}^{13}$ dimensions and demonstrates a communication complexity advantage, requiring less than $(0.696\ifmmode\pm\else\textpm\fi{}0.006)$ times the communication of any causally ordered protocol. These results elucidate the crucial role of the coherence of communication direction in achieving the exponential separation for the one-way processing task, and open a new path for experimentally exploring the fundamentals and applications of advanced features of indefinite causal structures.