Article
Computer Science, Artificial Intelligence
Ryan Sweke, Markus S. Kesselring, Evert P. L. van Nieuwenburg, Jens Eisert
Summary: Topological error correcting codes, particularly the surface code, are currently the most feasible roadmap for large-scale fault-tolerant quantum computation. This research shows that decoding such codes can be reformulated as interactions between a decoding agent and a code environment, using reinforcement learning to obtain decoding agents. By using deep Q learning, decoding agents for various simplified phenomenological noise models were obtained.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Thiago Serra, Teng Huang, Arvind U. Raghunathan, David Bergman
Summary: Quantum annealing (QA) is used to solve quadratic unconstrained binary optimization (QUBO) problems efficiently, but the limited connectivity of qubits in existing QA hardware has led to research on preprocessing algorithms for embedding problem variables into the hardware graph. This paper employs integer linear programming to search for embedding solutions, and introduces the concept of template embeddings for different classes of minors of the Chimera graph, improving upon existing methods based on odd cycle transversals. The study demonstrates that this approach can be used to certify the absence of a minor embedding using a specific template, achieving better scalability and significantly smaller runtimes compared to state-of-the-art OCT-based methods.
INFORMS JOURNAL ON COMPUTING
(2022)
Article
Quantum Science & Technology
Ruizhe Zhang, Guoming Wang, Peter Johnson
Summary: In this paper, we propose a quantum-classical hybrid algorithm that can efficiently estimate ground state properties of molecules and materials using low-depth quantum circuits. This algorithm is of great significance for practical applications.
Article
Quantum Science & Technology
Hannes Leipold, Federico M. Spedalieri
Summary: Recent advances in adiabatic quantum computing and quantum annealing have focused on using advanced and novel Hamiltonian representations to solve optimization problems. A significant advancement has been the development of driver Hamiltonians that can commute with the constraints of an optimization problem, providing an alternative method for satisfying those constraints. However, the design of successful driver Hamiltonians for multiple constraints currently relies on specific problem intuition and there is no simple general algorithm for generating them.
QUANTUM SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Theory & Methods
Anwar Said, Saeed-Ul Hassan, Suppawong Tuarob, Raheel Nawaz, Mudassir Shabbir
Summary: In this work, a distributed and permutation invariant graph embedding method called DGSD is proposed, which extracts graph representations on independently distributed machines by considering nodes' degree, common neighbors, and direct connectivity to find local proximity, suitable for processing large graphs with linear space complexity. Its scalability on sufficiently large random and real-world networks is demonstrated, along with its performance evaluation on bioinformatics and social networks in a distributed computing environment.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Information Systems
Muhammad Mudassar, Yanlong Zhai, Liao Lejian
Summary: Edge computing is a technology that pushes cloud computing capabilities to the edge of the network, improving the service quality for latency-oriented IoT applications. This article proposes a fault-tolerance methodology based on checkpointing and replication for edge computing, effectively increasing system reliability and task availability.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Physics, Multidisciplinary
Naihua Ji, Zhao Chen, Yingjie Qu, Rongyi Bao, Xin Yang, Shumei Wang
Summary: The article discusses the challenge of finding an efficient decoder for quantum error correction codes for fault-tolerant experiments in quantum computing. The study aims to develop a better decoding scheme based on the flag-bridge fault tolerance experiment. The research compares two decoding algorithms, a deep neural network decoding scheme and a recurrent neural network decoding scheme based on the belief propagation algorithm variant MBP4 algorithm. Experimental results showed that the decoding scheme developed in the study improved the pseudo-threshold by 39.52% compared to the minimum-weight perfect matching decoder. The findings suggest that the proposed decoding schemes could improve quantum error correction and fault-tolerant experiments in quantum computing.
FRONTIERS IN PHYSICS
(2023)
Article
Quantum Science & Technology
Suzhen Yuan, Shengwei Gao, Chao Wen, Yuchan Wang, Hong Qu, Yan Wang
Summary: This paper proposes a novel efficient fault-tolerant quantum divider based on Clifford+T gates and the long division algorithm. By designing efficient quantum subtractors and utilizing a quantum comparator, the proposed quantum divider outperforms existing work in terms of quantum cost, T-depth, T-count, and other metrics.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Computer Science, Theory & Methods
F. Orts, A. M. Puertas, G. Ortega, E. M. Garzon
Summary: This paper introduces quantum computing and focuses on adiabatic quantum computing as a quantum programming model. It has been successfully applied in solving problems like graph partitioning, traffic routing, and task scheduling. Specifically, the paper discusses the scheduling of unrelated parallel machines and extends the model to consider task priorities and delays. The proposed quantum nonlinear programming framework solves the Quadratic Unconstrained Binary Optimization problem on the D-Wave platform. It also utilizes compact models to handle larger scheduling problems and estimates the required qubits based on scheduling parameters.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Physics, Multidisciplinary
Chen-Fu Chiang, Paul M. Alsing
Summary: We investigate the irreconcilability issue of translating the search algorithm from the CTQW framework to the AQC framework. By modifying the catalyst Hamiltonian and introducing adaptive scheduling, we resolve the constant energy gap issue and improve the adiabatic local search in AQC.
Article
Computer Science, Artificial Intelligence
Lixin Cui, Ming Li, Lu Bai, Yue Wang, Jing Li, Yanchao Wang, Zhao Li, Yunwen Chen, Edwin R. Hancock
Summary: This paper proposes a novel framework for computing Quantum-based Entropic Representations (QBER) for un-attributed graphs using Continuous-time Quantum Walk (CTQW). By transforming each original graph into a family of k-level neighborhood graphs, the framework captures multi-level topological information of the original global graph. The structure of each neighborhood graph is characterized using the Average Mixing Matrix (AMM) of CTQW, enabling the computation of Quantum Shannon Entropy and entropic signature. Experimental results demonstrate the effectiveness of the proposed approach in classification accuracies, outperforming other entropic complexity measuring methods, graph kernel methods, and graph deep learning methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Hardware & Architecture
Jonathan M. Baker, Casey Duckering, David I. Schuster, Frederic T. Chong
Summary: Fault-tolerant quantum computing is essential for many quantum applications, and recent advancements have shown that virtualized logical qubit systems can achieve fault tolerance comparable to conventional 2-D transmon-only architectures using fewer resources.
Article
Computer Science, Hardware & Architecture
F. Orts, G. Ortega, A. C. Cucura, E. Filatovas, E. M. Garzon
Summary: This paper presents a quantum circuit for image binarization based on two novel comparators, which have been compared with other state-of-the-arts comparators to show that they are the best option in cases where noise is a problem and reduction is necessary.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Mike Gillard, Tommaso Benacchio
Summary: This paper investigates the impact of bit-flips and hardware failures on numerical simulation systems and algorithms in high performance computing facilities. A new method called FT-GCR is proposed to detect and recover from soft faults. Numerical experiments on an elliptic problem demonstrate the effectiveness of FT-GCR in various scenarios.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Multidisciplinary Sciences
Joshua J. Goings, Alec White, Joonho Lee, Christofer S. Tautermann, Matthias Degroote, Craig Gidney, Toru Shiozaki, Ryan Babbush, Nicholas C. Rubin
Summary: An accurate assessment of the potential computational advantages of quantum computers in chemical simulation is crucial for their deployment. This study explores the resources required for assessing the electronic structure of cytochrome P450 enzymes using quantum and classical computations, defining a boundary for classical-quantum advantage. The results show that simulation of large-scale CYP models has the potential to be a quantum advantage problem, emphasizing the interplay between classical computations and quantum algorithms in chemical simulation.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Mathematics, Applied
Kyle Kloster, Daniel Kral, Blair D. Sullivan
LINEAR ALGEBRA AND ITS APPLICATIONS
(2018)
Article
Quantum Science & Technology
Timothy D. Goodrich, Blair D. Sullivan, Travis S. Humble
QUANTUM INFORMATION PROCESSING
(2018)
Article
Multidisciplinary Sciences
Eugene F. Dumitrescu, Allison L. Fisher, Timothy D. Goodrich, Travis S. Humble, Blair D. Sullivan, Andrew L. Wright
Article
Computer Science, Hardware & Architecture
Erik D. Demaine, Felix Reidl, Peter Rossmanith, Fernando Sanchez Villaamil, Somnath Sikdar, Blair D. Sullivan
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2019)
Article
Mathematics, Applied
Eric Horton, Kyle Kloster, Blair D. Sullivan
LINEAR ALGEBRA AND ITS APPLICATIONS
(2019)
Article
Computer Science, Software Engineering
Jeremy Kun, Michael P. O'Brien, Marcin Pilipczuk, Blair D. Sullivan
Summary: Low-treedepth colorings are essential for algorithms utilizing structure in bounded expansion classes, ensuring subgraphs with few colors have limited treedepth. P-linear colorings are introduced as an alternative to commonly used p-centered colorings, efficiently computable in bounded expansion classes with color usage not exceeding that of p-centered colorings.
Article
Computer Science, Hardware & Architecture
Pal Gronas Drange, Markus Sortland Dregi, Daniel Lokshtanov, Blair D. Sullivan
Summary: Research has shown that both THRESHOLD EDITING and CHAIN EDITING problems are NP-hard, with quadratic vertex kernels, and can be solved using subexponential time parameterized algorithms. Threshold and chain graphs are the only known graph classes where all three versions (completion, deletion, and editing) are both NP-complete and solvable in subexponential parameterized time.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2022)
Article
Biology
Daniel S. Himmelstein, Michael Zietz, Vincent Rubinetti, Kyle Kloster, Benjamin J. Heil, Faisal Alquaddoomi, Dongbo Hu, David N. Nicholson, Yun Hao, Blair D. Sullivan, Michael W. Nagle, Casey S. Greene
Summary: Hetnets, or heterogeneous networks, are networks that contain multiple node and relationship types and can encode biomedical knowledge. This study introduces a new method that can extract important paths between any two nodes without requiring a supervised gold standard. By analyzing the node degree, the algorithm identifies path types that occur more frequently than random.
Article
Biotechnology & Applied Microbiology
C. Titus Brown, Dominik Moritz, Michael P. O'Brien, Felix Reidl, Taylor Reiter, Blair D. Sullivan
Proceedings Paper
Computer Science, Artificial Intelligence
Blair D. Sullivan, Andrew van der Poel, Trey Woodlief
ANALYSIS OF EXPERIMENTAL ALGORITHMS, SEA2 2019
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Erik D. Demaine, Timothy D. Goodrich, Kyle Kloster, Brian Lavallee, Quanquan C. Liu, Blair D. Sullivan, Ali Vakilian, Andrew van der Poel
27TH ANNUAL EUROPEAN SYMPOSIUM ON ALGORITHMS (ESA 2019)
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Alex J. Chin, Timothy D. Goodrich, Michael P. O'Brien, Felix Reidl, Blair D. Sullivan, Andrew van der Poel
2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
(2016)
Article
Computer Science, Information Systems
Robert A. Bridges, John Collins, Erik M. Ferragut, Jason Laska, Blair D. Sullivan
SOCIAL NETWORK ANALYSIS AND MINING
(2016)
Article
Computer Science, Theory & Methods
Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney
INTERNET MATHEMATICS
(2016)
Proceedings Paper
Computer Science, Information Systems
Matthew Farrell, Timothy D. Goodrich, Nathan Lemons, Felix Reidl, Fernando Sanchez Villaamil, Blair D. Sullivan
ALGORITHMS AND MODELS FOR THE WEB GRAPH, (WAW 2015)
(2015)
