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QUARK: A Framework for Quantum Computing Application Benchmarking [article]

Jernej Rudi Finžgar, Philipp Ross, Johannes Klepsch, Andre Luckow
2022 arXiv   pre-print
Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning.  ...  We propose an application-centric benchmark method and the QUantum computing Application benchmaRK (QUARK) framework to foster the investigation and creation of application benchmarks for QC.  ...  Additionally, we thank AWS, specifically Kyle Brubaker, Helmut Katzgraber, Henry Montagu, Mauricio Resende, and Martin Schuetz, for providing a QUBO formulation of the TSP.  ... 
arXiv:2202.03028v2 fatcat:p4jc4oamnfcb3pfdzdbzr573fu

Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes

Fabio Daolio, Arnaud Liefooghe, Sébastien Verel, Hernán Aguirre, Kiyoshi Tanaka
2017 Evolutionary Computation  
In this paper, we attempt to understand and to contrast the impact of problem features on the performance of randomized search heuristics for black-box multi-objective combinatorial optimization problems  ...  At first, we measure the performance of two conventional dominance-based approaches with unbounded archive on a benchmark of enumerable binary optimization problems with tunable ruggedness, objective space  ...  The authors are grateful to the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1162/evco_a_00193 pmid:27689467 fatcat:7lwr4sdq3fajtnni53vdkibd6a

Problem features vs. algorithm performance on rugged multi-objective combinatorial fitness landscapes

Fabio Daolio, Arnaud Liefooghe, Sébastien Verel, Hernán Aguirre, Kiyoshi Tanaka
2017 ACM SIGEVOlution  
In this paper, we attempt to understand and to contrast the impact of problem features on the performance of randomized search heuristics for black-box multi-objective combinatorial optimization problems  ...  At first, we measure the performance of two conventional dominance-based approaches with unbounded archive on a benchmark of enumerable binary optimization problems with tunable ruggedness, objective space  ...  The authors are grateful to the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1145/3066862.3066868 fatcat:74iaknghpjg7fplgvxrnpi6yeq

Ising Machine Based on Electrically Coupled Spin Hall Nano-Oscillators

Brooke C. McGoldrick, Jonathan Z. Sun, Luqiao Liu
2022 Physical Review Applied  
The Ising machine is an unconventional computing architecture that can solve NP-hard combinatorial optimization problems more efficiently than traditional von Neumann computing architectures.  ...  The spin Hall nano-oscillator has potential as a building block for a high-speed, low-power Ising machine based on its GHz operating frequency, sub-micron dimensions, and high degree of tunability.  ...  Simulated annealing algorithms are relatively easy to implement and can model a wide array of combinatorial optimization problems.  ... 
doi:10.1103/physrevapplied.17.014006 fatcat:uopviwv5qjbotji4gt3fca2nvy

Harnessing Intrinsic Noise in Memristor Hopfield Neural Networks for Combinatorial Optimization [article]

Fuxi Cai, Suhas Kumar, Thomas Van Vaerenbergh, Rui Liu, Can Li, Shimeng Yu, Qiangfei Xia, J. Joshua Yang, Raymond Beausoleil, Wei Lu, John Paul Strachan
2019 arXiv   pre-print
We describe a hybrid analog-digital computing approach to solve important combinatorial optimization problems that leverages memristors (two-terminal nonvolatile memories).  ...  We provide experimental demonstrations solving NP-hard max-cut problems directly in analog crossbar arrays, and supplement this with experimentally-grounded simulations to explore scalability with problem  ...  Acknowledgements We are grateful to Salvatore Mandra for performing the CPU simulations used in Table 1  ... 
arXiv:1903.11194v2 fatcat:5idqg4lzdrbihn6e6hippws6am

Quantum Simulators: Architectures and Opportunities

Ehud Altman, Kenneth R. Brown, Giuseppe Carleo, Lincoln D. Carr, Eugene Demler, Cheng Chin, Brian DeMarco, Sophia E. Economou, Mark A. Eriksson, Kai-Mei C. Fu, Markus Greiner, Kaden R.A. Hazzard (+25 others)
2021 PRX Quantum  
Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Commerce, the U.S.  ...  Department of Energy, or the United States Government.  ...  Applications to computer science could include hybrid digital-analog quantum computing, quantum approaches to combinatorial optimization problems, and quantum machine learning [21] .  ... 
doi:10.1103/prxquantum.2.017003 fatcat:e3g43oz4cvehvfu6q4bsduncby

Quantum Simulators: Architectures and Opportunities [article]

Ehud Altman, Kenneth R. Brown, Giuseppe Carleo, Lincoln D. Carr, Eugene Demler, Cheng Chin, Brian DeMarco, Sophia E. Economou, Mark A. Eriksson, Kai-Mei C. Fu, Markus Greiner, Kaden R. A. Hazzard, Randall G. Hulet (+23 others)
2019 arXiv   pre-print
effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multi-disciplinary collaborations with resources for quantum simulator software, hardware, and  ...  Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices.  ...  Acknowledgments: This material is based upon work supported by the National Science  ... 
arXiv:1912.06938v2 fatcat:mwsuktzfpzan7ol26etyrbystu

Energy Landscapes of Atomic Clusters as Black Box Optimization Benchmarks

C. L. Müller, I. F. Sbalzarini
2012 Evolutionary Computation  
We present the energy minimization of atomic clusters as a promising problem class for continuous black box optimization benchmarks.  ...  The resulting collection of landscapes is composed of smooth and rugged single-funnel topologies, as well as tunable double-funnel topologies.  ...  Acknowledgments We thank the three anonymous reviewers for their comments which considerably improved the quality of the manuscript.  ... 
doi:10.1162/evco_a_00086 pmid:22779442 fatcat:2w37ru3e2baozijiljtxknstqu

Quantum Computer Systems for Scientific Discovery [article]

Yuri Alexeev, Dave Bacon, Kenneth R. Brown, Robert Calderbank, Lincoln D. Carr, Frederic T. Chong, Brian DeMarco, Dirk Englund, Edward Farhi, Bill Fefferman, Alexey V. Gorshkov, Andrew Houck (+12 others)
2020 arXiv   pre-print
The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications.  ...  In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the  ...  It complements and supports parallel goals of two recent related NSF Convergence Accelerator Workshops: "Quantum Simulators: Architectures and Opportunities" in the area of quantum simulation [9] and  ... 
arXiv:1912.07577v3 fatcat:bqsckxj3bjgirhafvmodove43u

Quantum Computer Systems for Scientific Discovery

Yuri Alexeev, Dave Bacon, Kenneth R. Brown, Robert Calderbank, Lincoln D. Carr, Frederic T. Chong, Brian DeMarco, Dirk Englund, Edward Farhi, Bill Fefferman, Alexey V. Gorshkov, Andrew Houck (+12 others)
2021 PRX Quantum  
The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications.  ...  In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the  ...  Such universal quantum simulations can be used to find equilibrium properties of arbitrary Hamiltonians but also allow the more difficult problem of evolving them in time for simulations of quantum dynamics  ... 
doi:10.1103/prxquantum.2.017001 fatcat:r6gp3pyakncihdspagxyccccyy

From near to eternity: Spin-glass planting, tiling puzzles, and constraint-satisfaction problems

Firas Hamze, Darryl C. Jacob, Andrew J. Ochoa, Dilina Perera, Wenlong Wang, Helmut G. Katzgraber
2018 Physical review. E  
We present a methodology for generating Ising Hamiltonians of tunable complexity and with a priori known ground states based on a decomposition of the model graph into edge-disjoint subgraphs.  ...  The construction is shown to be equivalent to a type of three-dimensional constraint satisfaction problem known as the tiling puzzle.  ...  DMR-1151387) and thanks OzzyMan Reviews for being an important source of scientific information, as well as Wakatobi for their hospitality during the final stages of this manuscript.  ... 
doi:10.1103/physreve.97.043303 pmid:29758754 fatcat:y2zr4x3jlbd4bcrdrmqdxge7wm

An Open Framework for Constructing Continuous Optimization Problems

Changhe Li, Trung Thanh Nguyen, Sanyou Zeng, Ming Yang, Min Wu
2018 IEEE Transactions on Cybernetics  
However, there is no unified framework for constructing these types of problems and possible properties of many problems are not fully tunable.  ...  Abstract-Many artificial benchmark problems have been proposed for different kinds of continuous optimization, e.g., global optimization, multi-modal optimization, multi-objective optimization, dynamic  ...  Experimental Setup For GOPs, we select five traditional algorithms: two of them are the standard particle swarm optimization (PSO) [5] with a local-best model (PSO/L) and the global-best model (PSO/G  ... 
doi:10.1109/tcyb.2018.2825343 pmid:29993762 fatcat:vixsoejfb5fg5kqubozkghmkyy

Benchmarking in Optimization: Best Practice and Open Issues [article]

Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore (+5 others)
2020 arXiv   pre-print
As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic updates.  ...  The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers.  ...  Acknowledgments This work has been initiated at Dagstuhl seminar 19431 on Theory of Randomized Optimization Heuristics, 29 and we gratefully acknowledge the support of the Dagstuhl seminar center to our  ... 
arXiv:2007.03488v2 fatcat:yght6qkvxfdfxosdafd6bd53i4

A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration [article]

Vadim N. Smelyanskiy, Eleanor G. Rieffel, Sergey I. Knysh, Colin P. Williams, Mark W. Johnson, Murray C. Thom, William G. Macready, Kristen L. Pudenz
2012 arXiv   pre-print
We describe combinatorial optimization algorithms for task assignment in the context of autonomous unmanned exploration.  ...  In this article we describe the architecture of the D-Wave One machine and report its benchmarks.  ...  The efficacy of these approaches is generally determined by running them on benchmark sets of problem instances.  ... 
arXiv:1204.2821v2 fatcat:yogef3xqfjbmhfis4oyq2ldm3m

General Methodology for Soft-Error-Aware Power Optimization Using Gate Sizing

F. Dabiri, A. Nahapetian, T. Massey, M. Potkonjak, M. Sarrafzadeh
2008 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  
formulation that can be solved efficiently. 3) Many of the optimal existing techniques for gate sizing deal with an exponential number of paths in the circuit.  ...  Recently, as the feature size of logic gates (and transistors) is becoming smaller and smaller, the effect of soft-error rates caused by single-event upsets (SEUs) is becoming exponentially greater.  ...  Consequently, global optimization problems are typically quite difficult to solve; in the context of combinatorial problems, they are often NP-hard.  ... 
doi:10.1109/tcad.2008.2003268 fatcat:i6bfum2mi5d6hccblpxqltxqva
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