The Experts below are selected from a list of 132048 Experts worldwide ranked by ideXlab platform
J. B. Wang - One of the best experts on this subject based on the ideXlab platform.
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A quantum walk-assisted Approximate Algorithm for bounded NP optimisation problems
Quantum Information Processing, 2019Co-Authors: S. Marsh, J. B. WangAbstract:This paper describes an application of the quantum Approximate optimisation Algorithm (QAOA) to efficiently find Approximate solutions for computational problems contained in the polynomially bounded NP optimisation complexity class (NPO PB). We consider a generalisation of the QAOA state evolution to alternating quantum walks and solution-quality-dependent phase shifts and use the quantum walks to integrate the problem constraints of NPO problems. We apply the concept of a hybrid quantum-classical variational scheme to attempt finding the highest expectation value, which contains a high-quality solution. We synthesise an efficient quantum circuit for the constrained optimisation Algorithm, and we numerically demonstrate the behaviour of the circuit with respect to an illustrative NP optimisation problem with constraints, minimum vertex cover. With examples, this paper demonstrates that the degree of accuracy to which the quantum walks are simulated can be treated as an additional optimisation parameter, leading to improved results.
S. Marsh - One of the best experts on this subject based on the ideXlab platform.
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A quantum walk-assisted Approximate Algorithm for bounded NP optimisation problems
Quantum Information Processing, 2019Co-Authors: S. Marsh, J. B. WangAbstract:This paper describes an application of the quantum Approximate optimisation Algorithm (QAOA) to efficiently find Approximate solutions for computational problems contained in the polynomially bounded NP optimisation complexity class (NPO PB). We consider a generalisation of the QAOA state evolution to alternating quantum walks and solution-quality-dependent phase shifts and use the quantum walks to integrate the problem constraints of NPO problems. We apply the concept of a hybrid quantum-classical variational scheme to attempt finding the highest expectation value, which contains a high-quality solution. We synthesise an efficient quantum circuit for the constrained optimisation Algorithm, and we numerically demonstrate the behaviour of the circuit with respect to an illustrative NP optimisation problem with constraints, minimum vertex cover. With examples, this paper demonstrates that the degree of accuracy to which the quantum walks are simulated can be treated as an additional optimisation parameter, leading to improved results.
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a quantum walk assisted Approximate Algorithm for bounded np optimisation problems
arXiv: Quantum Physics, 2018Co-Authors: S. Marsh, Jingbo WangAbstract:This paper describes an application of the Quantum Approximate Optimisation Algorithm (QAOA) to efficiently find Approximate solutions for computational problems contained in the polynomially bounded NP optimisation complexity class (NPO PB). We consider a generalisation of the QAOA state evolution to alternating quantum walks and solution-quality-dependent phase shifts, and use the quantum walks to integrate the problem constraints of NPO problems. We apply the recent concept of a hybrid quantum-classical variational scheme to attempt finding the highest expectation value, which contains a high-quality solution. The Algorithm is applied to the problem of minimum vertex cover, showing promising results using only a fixed and low number of optimisation parameters.
Pan Zhang - One of the best experts on this subject based on the ideXlab platform.
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contracting arbitrary tensor networks general Approximate Algorithm and applications in graphical models and quantum circuit simulations
Physical Review Letters, 2020Co-Authors: Feng Pan, Pengfei Zhou, Pan ZhangAbstract:We present a general method for Approximately contracting tensor networks with an arbitrary connectivity. This enables us to release the computational power of tensor networks to wide use in inference and learning problems defined on general graphs. We show applications of our Algorithm in graphical models, specifically on estimating free energy of spin glasses defined on various of graphs, where our method largely outperforms existing Algorithms, including the mean-field methods and the recently proposed neural-network-based methods. We further apply our method to the simulation of random quantum circuits and demonstrate that, with a trade-off of negligible truncation errors, our method is able to simulate large quantum circuits that are out of reach of the state-of-the-art simulation methods.
Wang Ying - One of the best experts on this subject based on the ideXlab platform.
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a distributed protocol for ensuring both probabilistic coverage and connectivity of high density wireless sensor networks
Wireless Communications and Networking Conference, 2008Co-Authors: Tian Ying, Zhang Shufang, Wang YingAbstract:Coverage and connectivity are both attractive issues in wireless sensor networks. Both of them are important measurements of quality of service (Qos). Coverage configuration is an effective method to alleviate the energy-limitation problem of sensor nodes, while network connectivity is an unelectable problem guaranteeing the network usefulness. Existing researches promoted many protocols to configure network for coverage preserving or network connectivity or both of them. Our paper differs from these existing protocols in four key ways: (1) We proposed a distributed probabilistic coverage-preserving configuration protocol (DPCCP) based on Neyman-Peason probabilistic detection model; (2) A simplified Algorithm on coverage check is developed using Voronoi diagram; (3) Considering the network connectivity, we integrate DPCCP with SPAN to ensure both probabilistic coverage and network connectivity; (4) To evaluate the coverage percentage of our protocol, we propose an Approximate Algorithm. Simulation results show that DPCCP+SPAN can effectively reduce the number of active sensors and prolong the network lifetime on the precondition of probabilistic coverage-preserving and network connectivity.
Adam M Phillippy - One of the best experts on this subject based on the ideXlab platform.
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a fast Approximate Algorithm for mapping long reads to large reference databases
Journal of Computational Biology, 2018Co-Authors: Adam M PhillippyAbstract:Abstract Emerging single-molecule sequencing technologies from Pacific Biosciences and Oxford Nanopore have revived interest in long-read mapping Algorithms. Alignment-based seed-and-extend methods demonstrate good accuracy, but face limited scalability, while faster alignment-free methods typically trade decreased precision for efficiency. In this article, we combine a fast Approximate read mapping Algorithm based on minimizers with a novel MinHash identity estimation technique to achieve both scalability and precision. In contrast to prior methods, we develop a mathematical framework that defines the types of mapping targets we uncover, establish probabilistic estimates of p-value and sensitivity, and demonstrate tolerance for alignment error rates up to 20%. With this framework, our Algorithm automatically adapts to different minimum length and identity requirements and provides both positional and identity estimates for each mapping reported. For mapping human PacBio reads to the hg38 reference, our m...
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a fast Approximate Algorithm for mapping long reads to large reference databases
Research in Computational Molecular Biology, 2018Co-Authors: Chirag Jain, Alexander T Dilthey, Sergey Koren, Srinivas Aluru, Adam M PhillippyAbstract:Emerging single-molecule sequencing technologies from Pacific Biosciences and Oxford Nanopore have revived interest in long read mapping Algorithms. Alignment-based seed-and-extend methods demonstrate good accuracy, but face limited scalability, while faster alignment-free methods typically trade decreased precision for efficiency. In this paper, we combine a fast Approximate read mapping Algorithm based on minimizers with a novel MinHash identity estimation technique to achieve both scalability and precision. In contrast to prior methods, we develop a mathematical framework that defines the types of mapping targets we uncover, establish probabilistic estimates of p-value and sensitivity, and demonstrate tolerance for alignment error rates up to 20%. With this framework, our Algorithm automatically adapts to different minimum length and identity requirements and provides both positional and identity estimates for each mapping reported. For mapping human PacBio reads to the hg38 reference, our method is 290x faster than BWA-MEM with a lower memory footprint and recall rate of 96%. We further demonstrate the scalability of our method by mapping noisy PacBio reads (each \(\ge 5\) kbp in length) to the complete NCBI RefSeq database containing 838 Gbp of sequence and \(> 60,000\) genomes.
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a fast Approximate Algorithm for mapping long reads to large reference databases
bioRxiv, 2017Co-Authors: Chirag Jain, Alexander T Dilthey, Sergey Koren, Srinivas Aluru, Adam M PhillippyAbstract:Emerging single-molecule sequencing technologies from Pacific Biosciences and Oxford Nanopore have revived interest in long read mapping Algorithms. Alignment-based seed-and-extend methods demonstrate good accuracy, but face limited scalability, while faster alignment-free methods typically trade decreased precision for efficiency. In this paper, we combine a fast Approximate read mapping Algorithm based on minimizers with a novel MinHash identity estimation technique to achieve both scalability and precision. In contrast to prior methods, we develop a mathematical framework that defines the types of mapping targets we uncover, establish probabilistic estimates of p-value and sensitivity, and demonstrate tolerance for alignment error rates up to 20%. With this framework, our Algorithm automatically adapts to different minimum length and identity requirements and provides both positional and identity estimates for each mapping reported. For mapping human PacBio reads to the hg38 reference, our method is 290x faster than BWA-MEM with a lower memory footprint and recall rate of 96%. We further demonstrate the scalability of our method by mapping noisy PacBio reads (each ≥ 5 kbp in length) to the complete NCBI RefSeq database containing 838 Gbp of sequence and > 60,000 genomes.