TeNPy Forum

Thanks to [mention]mpsforphysics[/mention] for fixing this!

1) No, it doesn't. The problem is that bringing the state into canonical form during the DMRG run will screw up the environments. Thus, the `norm_tol` is only checking if the final state returned by DMRG is in canonical form. It tries to ensure the canonical form by performing a few more environment...

This jumping sure looks very wrong and strange. Any chance you could provide a simple example code where this happens? Do you expect a (nearly) degenerate ground state in this case? Than it might just be jumping around between different solutions. It might help to use 'chi_list' to gradually increas...

Hi Qicheng, how exactly do you want to extract the correlation length from the B? For a finite system, this is a somewhat ill-defined question, because you have no (guaranteed) periodicity. You might want to look at the correlation functions directly and fit an exponential to that. For an infinite s...

Exactly as in the example you posted: just specify an array/list as "strength" in add_coupling. In fact, you don't even need to define your own model, if you're happy with the XXZ Chain /SpinChain with alternating strength: from tenpy.models.xxz_chain import XXZChain model_params = { 'bc_M...

Welcome! The first argument of add_coupling is the strenght of the coupling. It can be a single value (if the strength is uniform all over the chain/lattice), but also a numpy array or something convertible a numpy array (which is the case here). The different values just indicate that the strength ...

Version 0.4.0 has been released. Before updating , please read the release notes . It is now supported to install TeNPy with pip via pip install --upgrade physics-tenpy # for 'stable' versions I'd recommend an installation with pip if you do not intend to modify the source files of TeNPy at any poin...

These operators do not commute here because they are acting on the same site. \sigma_i and \tau_i certianly do commute, which is obvious if you don't think in the grouped-site picture. In other terms, your hamiltonian is \begin{align} H &= J \sum_{<i,j>} (\sigma^x_i \sigma^x_j + \sigma^y_i \sig...

Writing this with Hermitian conjugates, I get: J_x S^x_i S^x_j + J_y S^y_i S^y_j = \left( \frac{J_x + J_y}{4} \right)(S^+_i S^-_j + (S^+_j S^-_i)^\dagger) + \left( \frac{J_x - J_y}{4} \right)(S^+_i S^+_j + (S^+_j S^+_i)^\dagger) Sorry, but I disagree. Using h.c. it reads: J_x S^x_i S^x_j + J_y S^y_...

The _data attribute of an tenpy.linalg.np_conserved.Array is a list with individual blocks of the tensor. Even without charges, there can be subblocks defined. this is the case for an MPO to exploit the sparseness of an MPO - if you write the Matrix, there are usually lot's of zeros, e.g. in your ca...

Qottmann wrote: ↑29 Apr 2019, 15:03 ... but at least it is running now.

Glad to hear that

Did you do the sudo apt-get install build-essential?
Or in other words, do you have a working C++ compiler?

This looks like a linking error. What OS are you using? Can you compile the first minimal example from http://docs.cython.org/en/latest/src/quickstart/build.html? Another minimal example with numpy: save this code as "a.pyx". cimport numpy as cnp import numpy as np cnp.import_array() def m...

Implemented

unfortunately, this is currently not implemented in TeNPy :( What you need to do is to write a custom tenpy.networks.mpo.MPOEnvironment which stores the tensors to disk instead of keeping them in the _LP and _RP lists. It should suffice to overwrite the get_LP, get_RP, set_LP, set_RP , del_LP, del_R...

Currently, there is nothing to directly support this in TeNPy. What you need to do is implement your own lattice (as a subclass of tenpy.models.lattice.Lattice ). Example: import numpy as np from tenpy.models import lattice from tenpy.networks import site class MyLattice(lattice.Lattice): def __init...

Agreed. The Triangular lattice is basically just a simple square lattice with one of the basis vectors rotated by 30 degrees towards the other. I'd suggest to use basis = np.array([[0.5 *np.sqrt(3), 0.5], # first basis vector [0., 1.]]) # second basis vector: y-direction If you quickly write down th...

Currently, the swapping for TEBD is not supported, although it should not be too difficult to implement, following the way you have outlined. There are a few things to be careful about, though: You need to adjust the existing model code to get the exponentials of the NNN terms. avoid onsite terms ap...

What exactly do you mean by computing the entanglement eigenvectors? The eigenvectors of which matrix? For a finite system, I could imagine to look at individual Schmidt vectors (=singular vectors) on the left or right of a given cut in the form of an MPS. You can obtain them by just the part of the...

Good question, thanks for pointing that out. The dominant eigenvector with eigenvalue 1 should always be in 0 charge sector: in canonical form the dominant eigenvector is just the identity, which has no charge. But I agree that it is not clear a priori (at least not to me) in which sector the next t...

Short answer : Try measuring correlation functions, or expectation values of |M| or M^2 instead of M=\sum_i X_i ;) Reason : You try to observe spontaneous symmetry breaking. By definition, spontaneous symmetry meaking means that you have a degenerate ground state (in the thermodynamic limit): the s...

In iDMRG, the iMPS unit cell is the central part of a growing environment on the left and right. The idea of the Engine.init_env() is to re-use the environment (and state) from a previous run. If the state has a sufficiently large correlation length, the environmnet "pins" DMRG to find the...

Another serious bug, this time in the models.
The problem is/was that adding the hermitian conjugate with was not done correctly.
Further details at issue #23.

If you have implemented your own model, check that you do it correctly in your case

I found a serious bug in npc.inner . This bug could result in wrong expecation values and Lanczos/DMRG giving completely wrong results. Luckily, the bug was only on the github master branch for two weeks (since March 02), so I hope it didn't cause too many problems yet. In case you did a "git p...

Sorry, that's indeed true. The bug only appeared if you use a mixer and have a model without onsite terms.
Should be fine now

PS: You can also report problems like that as github issues.