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Latest Julia version is v0.1.21

View source on github

Latest C++ version is v3.1.3

Clone from github (preferred)

Download: tar.gz or zip

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kopcloud永久免费加速-快连加速器app

Julia Version Released
Version 3.1 Released

ITensor—Intelligent Tensor—is a library for implementing tensor network calculations. See the list of recent papers using ITensor.
Features include:

Ordering of tensor indices is handled automatically
Full-featured matrix product state / tensor train and DMRG layer
Quantum number conserving (block-sparse) tensors; same interface as dense tensors
Complex numbers handled automatically and efficiently
Easy to install, with a friendly and productive interface

ITensors have an interface resembling tensor diagram notation, making them nearly as easy to multiply as scalars: tensors indices have unique identities and matching indices automatically contract when two ITensors are multiplied. This type of interface makes it simple to transcribe tensor network diagrams into correct, efficient code.

For example, the diagram below (resembling the overlap of matrix product states) can be converted to code as

Installing ITensor (Julia Version):

Install the Julia language
Enter the command julia to start an interactive Julia session
Inside this session, type ] to enter the package manager
Enter the command 618ip免费版
For more details, see our installation guide and read about getting started with ITensor.

Installing ITensor (C++ version):

Make sure you have an up-to-date C++17 compiler and LAPACK installed. On UNIX systems, use your package manager; on Mac OS install the free Xcode app from the app store; for Windows install cygwin.
Clone the latest version of ITensor:
```
git clone http://github.com/ITensor/ITensor itensor
```
(Or download the zip file if you do not have git.)
Cloning with git allows you to track changes to ITensor and is the preferred method; for more see our git quick start guide.
Create the options.mk file: cp options.mk.sample options.mk. Follow the instructions in this file to customize for your machine.
Type 618ip手机免费版 to build ITensor.
The compiled library files remain inside the ITensor source folder and are not put anywhere else on your machine. To create a program using ITensor, use the files in the "tutorial/project_template" folder as a starting point for making your own code.

For more details, read the full installation instructions.
Browse the documentation pages to learn more about ITensor.

We are grateful for generous support from the Simons Foundation.

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kopcloud永久免费加速-快连加速器app

auto a = Index(2), 
auto b = Index(2), 
auto c = Index(2);

auto Z = ITensor(a,b), 
auto X = ITensor(c,b);

Z.set(a=1,b=1, +1.0);
Z.set(a=2,b=2, -1.0);

X.set(b=1,c=2, +1.0);
X.set(b=2,c=1, +1.0);

//the * operator finds and
//contracts common index 'b'
//regardless of index order:

ITensor R = Z * X;

Print( elt(R,a=2,c=1) ); 

//output:
// elt(R,a=2,c=1) = -1

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auto i = Index(3);
auto j = Index(5);
auto k = Index(2);
auto l = Index(7);

auto A = ITensor(i,j,k);
auto B = ITensor(l,j);

A.set(i=1,j=1,k=1, 11.1);
A.set(i=2,j=1,k=2, -21.2);
A.set(k=1,i=3,j=1, 31.1);
A.set(k=1,i=1,j=2, 11.2);

B.set(j=2,l=4, 2.+4._i);

auto C = A * B;
PrintData(C);

618ip伕理官网 D = randomITensor(k,j,i);

auto R = A + D;

kopcloud永久免费加速-快连加速器app


auto i = Index(3,"i");
auto j = Index(7,"j");
auto k = Index(2,"k");
auto l = Index(4,"l");

auto T = randomITensor(i,j,k,l);

auto [U,S,V] = svd(T,i,k);

Print(norm(T - U*S*V));

//output: 
// norm(T - U*S*V) = 4.7458E-14

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DMRG is a powerful algorithm for finding dominant eigenvectors of exponentially large matrices (such as quantum Hamiltonians) in matrix product state (MPS) form. It is adaptive and converges very quickly and to high precision.

In addition to state-of-the-art DMRG codes, ITensor features the AutoMPO system, which is a powerful domain-specific language for creating matrix product operator (MPO) tensor networks as a sum of local terms, which is especially useful for quantum mechanics applications.

//Define Hilbert space of N spin-one sites
int N = 100;
auto sites = SpinOne(N);

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auto ampo = AutoMPO(sites);
for(int j = 1; j < N; ++j)
    {
    ampo += 0.5,"S+",j,"S-",j+1;
    ampo += 0.5,"S-",j,"S+",j+1;
    ampo +=     "Sz",j,"Sz",j+1;
    }
auto H = toMPO(ampo);

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//to be a product state
auto psi0 = MPS(InitState(sites,"Z0"));

//Perform 5 sweeps of DMRG
auto sweeps = Sweeps(5);
//Specify max number of states kept each sweep
sweeps.maxdim() = 50, 50, 100, 100, 200;

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auto [energy,psi] = dmrg(H,psi0,sweeps,"Quiet");

//Continue to analyze wavefunction afterward 
Print(inner(psi,H,psi)); //<psi|H|psi>

for(int j = 1; j <= N; ++j)
    {
    //Make site j the MPS "orthogonality center"
    psi.position(j);
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    Real Szj = elt(psi(j)
               * op(sites,"Sz",j)
               * dag(prime(psi(j),"Site")));
    println("Sz_",j," = ",Szj);
    }