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1Journal Of Computational Physics 1992: Vol 101 Contents

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2Journal Of Computational Physics 1971: Vol 7 Contents

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3Progress In Computational Physics Of Matter : Methods, Software And Applications

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4Journal Of Computational Physics 1976: Vol 20 Contents

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5Journal Of Computational Physics 1983: Vol 52 Contents

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6Journal Of Computational Physics 1990: Vol 90 Contents

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7Journal Of Computational Physics 1973: Vol 11 Contents

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8Journal Of Computational Physics 1990: Vol 91 Contents

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9Journal Of Computational Physics 1977: Vol 23 Contents

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10Computational Approaches In Condensed-matter Physics : Proceedings Of The 6th Nishinomiya-Yukawa Memorial Symposium, Nishinomiya, Japan, October 24 And 25, 1991

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11The Computational Status Of Physics: A Computable Formulation Of Quantum Theory

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According to the Church-Turing Thesis (CTT), effective formal behaviours can be simulated by Turing machines; this has naturally led to speculation that physical systems can also be simulated computationally. But is this wider claim true, or do behaviours exist which are strictly hypercomputational? Several idealised computational models are known which suggest the possibility of hypercomputation, some Newtonian, some based on cosmology, some on quantum theory. While these models' physicality is debatable, they nonetheless throw into question the validity of extending CTT to include all physical systems. We consider the physicality of hypercomputational behaviour from first principles, by showing that quantum theory can be reformulated in a way that explains why physical behaviours can be regarded as 'computing something' in the standard computational state-machine sense. While this does not rule out the physicality of hypercomputation, it strongly limits the forms it can take. Our model also has physical consequences; in particular, the continuity of motion and arrow of time become theorems within the basic model.

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12Journal Of Computational Physics 1983: Vol 50 Contents

According to the Church-Turing Thesis (CTT), effective formal behaviours can be simulated by Turing machines; this has naturally led to speculation that physical systems can also be simulated computationally. But is this wider claim true, or do behaviours exist which are strictly hypercomputational? Several idealised computational models are known which suggest the possibility of hypercomputation, some Newtonian, some based on cosmology, some on quantum theory. While these models' physicality is debatable, they nonetheless throw into question the validity of extending CTT to include all physical systems. We consider the physicality of hypercomputational behaviour from first principles, by showing that quantum theory can be reformulated in a way that explains why physical behaviours can be regarded as 'computing something' in the standard computational state-machine sense. While this does not rule out the physicality of hypercomputation, it strongly limits the forms it can take. Our model also has physical consequences; in particular, the continuity of motion and arrow of time become theorems within the basic model.

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13DTIC ADA517028: A Computational Cluster For Advanced Plasma Physics Simulations

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This DURIP equipment award was used to purchase, install, and bring on-line a computational cluster for plasma physics simulations. An SGI integrated cluster environment ICE unit was purchased, featuring 192 2.8 GHz processors (on 24 nodes) with 384 GB highspeed RAM on an Infiniband backbone. Additional nodes include an 8-processor login node (32 GB RAM) and a data server with a 6 TB RAID6 Infiniband system, plus admin and rack leader nodes. SGI personnel setup the system on-site, with additional integration performed by our local IT personnel. SGI also installed cluster control software and licenses for the SUSE-based Linux operating system. Software was purchased and installed including Intel Fortran and C/C++ compiler suites (debuggers, performance measurements, etc.), the TotalView parallel debugger, and the MOAB/PBS scheduling software. Comparisons were made amongst several cluster manufacturers, including Cray, IBM, Dell, Silicon Mechanics, Rackable Sytems, and SiCortex before deciding on the SGI ICE model as the most turn-key system. The system was ordered in late 2008 and arrived in early 2009 and was up and running routinely with local users in a few weeks. Local University of Washington computational groups have made extensive use of the DURIP cluster, running several different advanced extended MHD/two-fluid codes. This DURIP award has greatly increased turnaround time and productivity for these groups.

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14Computational Mathematics And Mathematical Physics 1992: Vol 32 Table Of Contents

Computational Mathematics and Mathematical Physics 1992: Volume 32 , Issue Contents. Digitized from IA1646504-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1991_31_12_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1992_32_1_0 .

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15DTIC ADA385793: Advanced Computational Physics Instrumentation

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An advanced program in Computational Physics can conduct research III students in broad areas of physics and computational technology, and be responsive to needs of the Department of Defense. The first serves the Departmental mission, the second gives the preparation for careers in physics or in informational technology (IT), and the third advances our military preparedness. We note that the IT sector currently has approximately one million unfilled opportunities in the United States. Three areas of computationally based research have been selected with these criteria. They are: (1) power compression technology, specifically the study of ultrahigh (approximately equal 10(exp 21) W/cu cm) power densities, (2) basic physical interactions, which has potential applications to navigation and code-breaking, and (3) protein dynamics, an area related to new potentialities in defense against biological attack. The computational equipment associated with this program have yielded results on: (A) the stability of self-trapped plasma channels produced with intense femtosecond ultraviolet (248 mm) pulses, (B) the mechanism of gamma-ray bursts, (C) the fusion of the concepts of mass and space, and (D) the potential existence of a new organizing-principle in biology that has a direct physical counterpart in the group structure of elementary particle states.

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16DTIC ADA553435: A Computational Efficient Physics Based Methodology For Modeling Ceramic Matrix Composites (Preprint)

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The purpose of this study is to demonstrate the feasibility of modeling inelastic responses of ceramic matrix composites (CMC), where deviations from elastic behavior are primarily due to micro-crack evolution within the material. The theoretical basis for this research is a physically based homogenization theory, termed discrete damage space homogenization method (DDSHM), which combines micromechanics and thermodynamics to determine the overall response functions of multi-phase materials of arbitrary complexity. The procedure is named DDSHM because the constitutive and evolution equations of the composite are obtained for discrete values of damage (numbers of cracks and crack lengths) and not in terms of an average damage parameter. The micro-crack evolution law used may in general include time and temperature effects as well as other material parameters including changes in constituent and interphase strengths under extended environmental exposures.

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17NASA Technical Reports Server (NTRS) 19870016575: Application Of Computational Physics Within Northrop

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An overview of Northrop programs in computational physics is presented. These programs depend on access to today's supercomputers, such as the Numerical Aerodynamical Simulator (NAS), and future growth on the continuing evolution of computational engines. Descriptions here are concentrated on the following areas: computational fluid dynamics (CFD), computational electromagnetics (CEM), computer architectures, and expert systems. Current efforts and future directions in these areas are presented. The impact of advances in the CFD area is described, and parallels are drawn to analagous developments in CEM. The relationship between advances in these areas and the development of advances (parallel) architectures and expert systems is also presented.

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18U.S.S.R. Computational Mathematics And Mathematical Physics 1987: Vol 27 Table Of Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

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19Journal Of Computational Physics 1971: Vol 8 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

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20Journal Of Computational Physics 1983: Vol 49 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

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21Journal Of Computational Physics 1975: Vol 17 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

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22Journal Of Computational Physics 1981: Vol 42 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

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23Journal Of Computational Physics 1989: Vol 80 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

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24Journal Of Computational Physics 1990: Vol 88 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

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25Computational Mathematics And Mathematical Physics 1996: Vol 36 Table Of Contents

Computational Mathematics and Mathematical Physics 1996: Volume 36 , Issue CONTENTS. Digitized from IA1646504-02 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1995_35_12_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1996_36_1_0 .

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26U.S.S.R. Computational Mathematics And Mathematical Physics 1986: Vol 26 Table Of Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1986: Volume 26 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_index . Next issue: sim_computational-mathematics-and-mathematical-physics_1986_26_1 .

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27Annual Reviews Of Computational Physics

v. : 23 cm

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28Computational Physics

Three good computational physics texts using Fortran90 and Java. Landau; KNA; Gould & T.

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29U.S.S.R. Computational Mathematics And Mathematical Physics 1990: Vol 30 Table Of Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1990: Volume 30 , Issue CONTENTS. Digitized from IA1652722-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1989_29_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1990_30_1 .

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30U.S.S.R. Computational Mathematics And Mathematical Physics 1978: Vol 18 Index & Table Of Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1978: Volume 18 , Issue INDEX/CONTENTS. Digitized from IA1646333-05 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1977_17_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1978_18_1 .

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31"Computational Complexity And Fundamental Physics" With Scott Aaronson Of MIT

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Presented by The Aspen Center for Physics Scott Aaronson is Schlumberger Chair of Computer Science and Director, Quantum Information Center, Department of Computer Science at The University of Texas at Austin. In summer 2022 he announced he would be working for a year at OpenAI on theoretical foundations of AI safety. Know Aspen. Watch GrassRoots. GrassRoots TV programming is entirely funded by direct contributions from members of the Roaring Fork Valley community, and through grants from the City of Aspen Community Non-Profit Grant Program, Pitkin County Healthy Community Fund, The Town of Snowmass Village and The Aspen Thrift Shop. Founded by citizens of Aspen, Colorado in 1972, GrassRoots TV is the first and oldest community operated and programmed open-source media center and television station in the United States. Virtually all of the programming is created by local residents, occasionally with world renown guests. In partnership with the American Archive of Public Media and the Library of Congress, GrassRoots is currently cataloguing, restoring and digitally archiving 500 of the most at-risk original video programs out of its 45,000 title Community Video Archive. Help sustain this community treasure by donating now! Go to: https://bit.ly/GRTVContribute Don't forget to SUBSCRIBE, HIT LIKE and leave a COMMENT to let us know if you enjoyed this video, it is important to us and the community for you to become part of the conversation. Thanks for tuning in! Download the GrassRoots Community Network iOS and Android apps Subscribe for more videos: https://bit.ly/2Ycpi4P Facebook: https://www.facebook.com/GrassRootsCommunityNetwork/ Twitter: https://twitter.com/grassrootstv Web: http://www.grassrootstv.org/

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32DTIC ADA507040: Application Of Numerical Methods To Solve Integral Equations Arising In Computational Physics/Electromagnetics

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The author, as part of an internship under the Joint Educational Opportunities for Minorities (JEOM) Program at Naval Air Warfare Center Weapons Division (NAWCWD), China Lake, California, has devised models and methods for the solution of certain frequently recurring problems in electrical engineering. Starting with fundamental equations relating to line charges, the author utilizes the well-known method of moments to model single strip charges, multiple strip charges in various geometrical configurations, and a charged square cylinder excited by an incidental plane wave. Finally, the author considers the problem of calculating the current along a thin-wire antenna.

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33U.S.S.R. Computational Mathematics And Mathematical Physics 1972: Vol 12 Index & Table Of Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1972: Volume 12 , Issue Contents/Index. Digitized from IA1646333-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1971_11_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1972_12_1 .

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34Journal Of Computational Physics 1992: Vol 102 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1972: Volume 12 , Issue Contents/Index. Digitized from IA1646333-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1971_11_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1972_12_1 .

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35Computational Physics : Simulation Of Classical And Quantum Systems

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U.S.S.R. Computational Mathematics and Mathematical Physics 1972: Volume 12 , Issue Contents/Index. Digitized from IA1646333-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1971_11_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1972_12_1 .

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36Journal Of Computational Physics 1976: Vol 22 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1972: Volume 12 , Issue Contents/Index. Digitized from IA1646333-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1971_11_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1972_12_1 .

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37Journal Of Computational Physics 1988: Vol 74 Contents

U.S.S.R. Computational Mathematics and Mathematical Physics 1972: Volume 12 , Issue Contents/Index. Digitized from IA1646333-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1971_11_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1972_12_1 .

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38Introduction To Computational Physics

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U.S.S.R. Computational Mathematics and Mathematical Physics 1972: Volume 12 , Issue Contents/Index. Digitized from IA1646333-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1971_11_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1972_12_1 .

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39Paul L De Vries A First Course In Computational Physics Wiley ( 1984)

computational physics book

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40Physics 210: Introduction To Computational Physics

computational physics book

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41DTIC ADA585145: Computational Methods For Multi-physics Applications With Fluid-structure Interaction

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Efficient modeling and computation of the nonlinear interaction of fluid with a solid undergoing nonlinear deformation has remained a challenging problem in computational science and engineering. Direct numerical simulation of the non-linear equations, governing even the most simplified fluid-structure interaction model depends on the convergence of iterative solvers which in turn relies heavily on the properties of the coupled system. The purpose of this work is to model and simulate multi-physics applications that involve fluid-structure interaction using a distributed multilevel algorithm with finite elements. The proposed algorithm is tested using COMSOL which offers the flexibility and efficiency to study coupled problems involving fluid-structure interaction. Numerical results for some benchmark fluid-structure interactions are presented that validate the proposed computational methodology for solving coupled problems involving fluid-structure interaction is reliable and robust.

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42Quantum Annealing: From Viewpoints Of Statistical Physics, Condensed Matter Physics, And Computational Physics

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In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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43Journal Of Computational Physics 1980: Vol 38 Contents

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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44Journal Of Computational Physics 1991: Vol 92 Contents

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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45Journal Of Computational Physics 1983: Vol 51 Contents

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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46Journal Of Computational Physics 1993: Vol 106 Contents

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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47Journal Of Computational Physics 1987: Vol 72 Contents

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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48Journal Of Computational Physics 1989: Vol 85 Contents

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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49Methods In Computational Physics

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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50Proceedings Of The Summer School Of Computational Atomic And Nuclear Physics, The University Of The South Sewanee, Tennessee, June 26-July 7, 1989

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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