kaihsin / Tor10

A Generic Tensor-Network library that is designed for quantum simulation, base on the pytorch

What's new

v0.3
1. Three type of UniTensor can be construct. [symmetry][tagged][untagged] 
2. The Bond can be tagged with bra/ket or untagged (regular)
3. Now change the storage of symmetry to blockform. the dense storage for symmetry is deprecated.
4. Add braketform() to coalesce the bra/ket with row/col(in/out)
5. rowrank defines the row/col space. 
6. Enhanced Print_diagram
7. Seperate Contiguous() and Contiguous\_() for inplace and return type function.
8. change behavior of GetBlock and PutBlock on a non-symm tensor. in this version, it will return a rank-2 matrix.
9. Add GetValidQnums() for symmetry tensor
10. For symmtry, the bond order is relevant. The default fusion tree is in order of ((((0,1),2),3),4 ....
11. Add UniTensor.dtype() [@property]
12. Add UniTensor.device() [@property]
13. nn , some linalg can only accept non-symm, untagged tensor. 

v0.3.1    
1. Fix cannot create rank-0 UniTensor bug.

v0.3.2
1. Fix Permute() will print the tensor elements bug.

v0.3.3
1. Remove the private args in UniTensor.__init__, in place, they are move to private member function __mac() 
2. Fix UniTensor.__pow__ does not return anything bug. 
3. Remove unrelated arguments in documentation
4. Change the order of functions appears in documentation tor10.UniTensor

v0.3.4
1. Fix Svd, Svd_truncate, Qr, Qdr crash bug. Originated from the variable change of v0.3.3a 
2. From 1., iTEBD.py cannot work is fixed. 

v0.3.5
1. Fix create Rank-0 Tensor crash bug, also change to real rank-0 tensor
2. Fix Reshape_() labels are not set bug, and remove redundancy code in the Reshape_()
3. Add View() and View_, which is the same functionality as pytorch 
4. Can now create rank-0 tensor from torch.tensor(). 

v0.3.6
1. Exchange BRA and KET. KET is now row-space and BRA is now col-space

v0.3.7
1. Change `N_rowrank` to `rowrank`
2. Fix T.Svd_truncate missing argument bug
3. Fix T.Svd_truncate missing linalg. bug
4. Fix PutBlock cause mismatch on non-contiguous tensors
5. Update docs for PutBlock

v0.3.8
1. Fix Network trace out all bonds will raise error bug.

v0.3.9
1. Change the behaviour of Bonds when initialize a UniTensor. All the bonds will be deepcopy now

v0.3.10
1. Change Tor10 -> tor10 for deployment

Release version

v0.3.9 alpha

Known issue

1. HOSVD is not functional
2. Contract for UniTensors with symmetry cannot have the bonds in two tensors all are unique labels.

Requirements

pytorch>=1.0
numpy  >=1.15
sphinx >=1.8.2
sphinx_rtd_theme >=0.4.2 

Documentation:

https://kaihsinwu.gitlab.io/tor10

Code naming principle:

1) the functions start with "_" are the private function that should not be call directly by user.

Feature:

1. Create Tensor:
    * support multiple precisions.        
    * support devices (cpu and gpu are trivial)
    * preserve the similar api for Bond 
    * can serve as regular generic Tensor or physical tensor (with bra-ket tagged)

       ## create a rank-2 Physical Tensor with no symmetry 
       bds = [ Bond(3,BD_BRA), Bond(4,BD_KET)]
       A = UniTensor(bds,label=[2,4],N_inbond=1,dtype=torch.float64,device=torch.device("cpu"))

       ## create a rank-2 generic Tensor 
       bds = [ Bond(4), Bond(6) ]
       B = UniTensor(bds,N_inbond=1)

       ## Moving to GPU:
       A.to(torch.device("cuda:0"))

2. Tensor :
    * vitual swap and permute. All the permute and swap will not change the underlying memory
    * Use Contiguous() or Contiguous_() when needed to actual moving the memory layout.

        A.Contiguous()

3. Multiple Symmetries:
    * Support arbitrary numbers and types of symmetry.
    * Currently support U1 and Zn (with arbitrary n). 

        #> Multiple mix symmetry: U1 x Z2 x Z4
        bd_sym_mix = tor10.Bond(3,qnums=[[-2,0,0],
                                         [-1,1,3],
                                         [ 1,0,2]],
                                 sym_types=[tor10.Symmetry.U1(),
                                            tor10.Symmetry.Zn(2),
                                            tor10.Symmetry.Zn(4)],
                                  tor10.BD_BRA)

4. Network :
    * See documentation for how to use network.

5. Autograd mechanism:
    The tor10 now support the autograd functionality. The Contract, Matmul etc will automatically contruct the gradient flow for UniTensor that has [requires_grad=True]
    
    * See documentation for further details


6. Easy coordinate with pytorch for Neural-Network:
    We provide tor10.nn that can easy cooperate with pytorch.nn.Module to perform neural-network tasks.

        import torch
        import tor10
        class Model(torch.nn.Module):
            def __init__(self):
                super(Model,self).__init__()
                ## Customize and register the parameter.
                self.P1 = tor10.nn.Parameter(tor10.UniTensor(bonds=[tor10.Bond(2),tor10.Bond(2)],rowrank=1))
                self.P2 = tor10.nn.Parameter(tor10.UniTensor(bonds=[tor10.Bond(2),tor10.Bond(2)],rowrank=1))
 
            def forward(self,x):
                y = tor10.Matmul(tor10.Matmul(x,self.P1),self.P2)
                return y

        md = Model()
        print(list(md.parameters()))
        ## Output:
        #    [Parameter containing:
        #    tensor([[0., 0.],
        #            [0., 0.]], dtype=torch.float64, requires_grad=True), Parameter containing:
        #    tensor([[0., 0.],
        #            [0., 0.]], dtype=torch.float64, requires_grad=True)]

    * See documentation for further details


See test.py for further detail application functions.

Example:

See iTEBD.py for an simple example of using iTEBD algo. to calculate the 1D-transverse field Ising model 
See iTEBD_gpu.py for an simple example of the same algo accelerated with GPU. 

See example.py for elementary usage.

Developers:

* Kai-Hsin Wu     [e-mail]

* Jing-Jer Yen 
* Yen-Hsin Wu