TeNPy Forum

Having one more up than down electron can only work if you have an odd number of sites. Then you can do, e.g, initial_state = ["up", "down"] * (M.lat.N_sites // 2) initial_state.append("up") If you have an even number of sites and want two more up than down electrons, ...

Thanks for pointing it out, I've updated it on the master branch.
Unfortunately, it didn't make it into the release of v0.6.0 any more, which I packed last Friday

1) It depends on the physical system, not just the lattice! E.g. in the dimer lattice of my previous post, you should define the ordering such that each dimer is nearest-neighbor in the MPS. 3.) The order you plotted here is different from the default one of the tenpy.models.lattice.Honeycomb lattic...

In the latest version of the master branch, the function definitely has the labels arguments, introduced in 8dd1f93b719074770a9d6cef16f0d89d9fd6229d on Jan. 27th. This change was introduced after the last official release of v0.5.0 on Dez.18 last year, however, so if you used the pip install physics...

The square lattice of Leon Balents' paper is a 45-degree rotation compared to the DualSquareLattice we are using. This implies a different geometry for the cylinder axis (going in the [1, 1] direction of our DualSquareLattice), and a different "cut" when you split the MPS between unit cell...

Yes, that's almost right. We're working on something like this in the exp_MPO branch on github. It's not quite ready for use, and we will adjust the interface to better match the other evolution algorithms, but it basically works as a preliminary version, and you can look at it to see how it works. ...

1) TeNPy only supports "open" MPS, since peridoic MPS don't have a canonical form. You can make your Model have periodic/toric boundary conditions and still have an open MPS, and TeNPy supports that. The price you pay is that you need the squared bond dimension due to the (expected) entang...

I'm not sure if the error estimate of arXiv:1711.01104 could be generalized to infinite systems, that would actually be quite usefull.

I finally took the time to implement at least the "naive" approach of contracting the network with the squared MPO for <psi| H H |psi> in the new variance . Right now it works only for finite systems - for infinite MPS, this is a whole new story, since there is no direct notion of overlaps...

Nice workaround! I'd formulate it slightly differnt, though. If we know that there is a translationally invariant, canonical MPS, let's call it C^i_{ab} , then it is related to the MPS tensors found by DMRG via a unitary matrix U : B_0^i = U C^i \quad\text{and}\quad B_1^i = C^i U^{\dagger}. In gener...

First of all: the segment [0, 1, 2, 3, 4, 5] is only equivalent to psi.entanglement_entropy()[6] on a finite system. In an infinite system, psi.entanglement_entropy()[6] takes region A to be [..., -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], which is a single cut dividing the system into left and right halfs,...

As long as you have more than one tensor in you MPS, they will be different, even with single site DMRG. DMRG optimizes the tensors one after another (in the "sweep"), which immediately breaks the translation invariance in the beginning. If you model is translation invariant and the ground...

Please include at least the error message for requests like this. It seems to be just a python issue, line 31 of the code above, lattice is not defined. When adding the line from tenpy.models import lattice in the beginning, it runs fine. I presume you want to extend the TFI model and add further te...

We allow a fractional filling n/L where n \in \mathbb{Z} is the number of particles, and L the number of sites in the MPS (or for infinite MPS: in the MPS unit cell). So a filling of 3/10 particles is "commensurate" in that sense. Incommensurate would be and average filling of an irration ...

Read the documentation at entanglement_entropy_segment . There is another parameter first_site , where this segment starts. If you use segment=[0, 1, 2] and don't specify first_site , it will default to the segments on sites [0, 1, 2] [1, 2, 3] [2, 3, 4] ... Each of them gives you one number, result...

where spin operator \mathbf{S}_i = \sum_{\sigma,\sigma'} b^\dagger_{\sigma,\sigma'} S_{\sigma,\sigma'} b_{\sigma,\sigma'} , with standard spin-1 matrice S_{\sigma,\sigma'} . I guess you meant the usual definiton S^\alpha_i = \sum_{\sigma,\sigma'} (b^\alpha_{i,\sigma})^\dagger S^\alpha_{\sigma,\sigm...

This is an overflow error; the norm of the `psi` increases with each tensor added. You can observe this by using psi.canonical_form_finite(renormalize=False) and printing psi.norm of the final state. The norm blows up exponentially with the number of tensors. Trying a few normalizations, I found tha...

Indeed, the guess should be very close to the actual ground state if DMRG converged well; this will lead to problems for Lanczos. Did choosing a different initial state help? You can also try to e.g. apply Sx or Sz operators on the initial guess of the ground state, if you have some idea of what the...

You want to simulate it on a finite system, right? The SSH chain is just a regular chain with alternating coupling strength. Given that, I think it might be easier in this case to really use the "Chain" with a single-site unit cell instead of a Lattice with a two-site unit cell. In particu...

(1): Looks good to me. In fact, that's code taken from CouplingMPOModel.init_lattice() for the case of a 2D lattice. (2): Yes. You might want to take a look at mps2lat_values in that case. Use it like this: exp_vals_mps = psi.expectation_value("N") exp_vals_lat = model.lat.mps2lat_values(e...

I think the question aims at how to get the terms contained in the kinetic energy. Actually, there's some functionality in the tenpy.models.model.CouplingModel , which is not explicitly used in most of the pre-defined models, though, and a bit hidden in the documentation. When one calls add_coupling...

You cannot/should not take the transfer matrix of a single site, if you have a two-site unit cell in your MPS. Sometimes, the charge values will not even allow you, and even if they do, it there is a different gauge on the two bonds - which can and will be the case if there are degenerate singular v...

BTW, in this sense, the normalization factors of the left (to square singular value) and right eigenvector (identity) of right canonical form should cancel each other out, right? However, in a real case it seems will change the largest amplitude of the transfer matrix (the largest eigenvalue will b...

It's just a matter of normalization.
Eigenvalues get normalized to np.linalg.norm(v) = 1, but in this case you want np.sum(v) = 1.

At the end of your code, add and compare the results, the prints should give the same

That's an excellent question! TeNPy supports 'segment' boundary conditions, which you can basically use to do that. What we had in mind for those boundary conditions is the folling: Run DMRG in an infinite system Extract or "cut out" a subsystem with a finite length from it, giving you an ...