TeNPy Forum

Since exponentiating a matrix is often needed for tensor networks, the scipy.linalg.expm function is also implemented for np_conserved Arrays . When you talk about string order parameters, do you really want to calculate that? What you wrote suggests that you try to calculate something like \langle ...

The "charges" you get are the "quantum numbers" you conserved. Of course, it depends on the model which "quantum numbers" you can actually preserve. For example, for a spin-model, you can preserve the Magnetization (e.g. Heisenberg XXZ chain), or just the parity of the ...

The general setup looks fine. Regarding the bond dimension, you need to carefully check if larger \chi leads to different results. I would be very surprised if 30 is enough for convergence. To check that it works you need to compare results you have with existing results in the literature ;) Finite ...

The correct permutation depends on the lattice you choose, and possibly on the "order" argument of that lattice. Yes, you should simply call it like psi.compute_K(perm=M.lat) # where M is your model to automatically figure out the "correct" permuation. This permutation will corre...

Sorry for not coming back to this earlier. The real question is: what do you mean with "random" when talking about an MPS? When you draw a "random" state uniformly from the Hilbert space, it has a volume law entanglement; in fact it is almost maximally entangled! Therefore, it wo...

What kind of "gate" do you have in mind? The usual TEBD where the "gates" are U=exp(i H_bond dt) is implemented in TeNPy.

On general grounds: DMRG is a variational method which looks for the best approximation of the ground state in the space of MPS with a given maximal bond dimension (assuming you put a "chi_max" in the DMRG_parameters). This class of MPS can only represent states with a entanglement entropy...

The problem was just that pickle needs the files to be opened in "binary" mode since Python 3, and the example stems from a time where TeNPy was still running with Python 2
Anyways, I fixed it (and some other issue it had), it should now work again in the newest version.

psi.expectation_value("Sx") tries to evaluate the expectation value \langle S^x_i\rangle on each site i of the MPS. If you called psi.group_sites(k) beforehand, the local sites i refer to GroupedSite , each containing k of the previous sites, and the "Sx" is no longer defined (o...

That's the point: if your result is not unique (due to the degeneracy), any (small) change of the setup can change the result - it's basically random, what you get. Your results should usually still be reproducable, when you run the same code on the same machine with the same python version, but cha...

The problem you have met comes from the degeneracy of the states - you have a bunch of correct, possible solutions, and which one is returned depends on numerical round off errors. I don't get the comment on truncation. Let me clarify: The truncation respects all the parameters chi_max, svd_min and ...

What's even more worrying is that the expectation values of say "Sz" are also changing :!: However, it's not a bug of TenPy, but there is a simple explanation for this behaviour :roll: The problem is that the ground state is degenerate (up to a finite size gap below the precision). You cho...

This is actually a common problem that makes very much sense is comes from the fact that DMRG optimizes locally (just two sites at once) and the special initial state - product states are very special, having no entanglement at all... People often say that DMRG is "stuck in a local minima"...

Hi Jason, Welcome to the forum :) very good question! In TeNPy, this is a bit hidden. Actually, the "state prediction" is in the line 103 of the toycode: theta0 = np.reshape(self.psi.get_theta2(i), [Heff.shape[0]]) # initial guess This line gets the "initial guess" for the ground...

That's a very good question, thanks for asking! Each of the different Site classes get their own instance of the ChargeInfo class, but all the sites involved into one tensor network (e.g. a MPS or MPO) need to have the same ChargeInfo. When you don't conserve the boson number, both sites agree by sa...

thank you very much, I've now included these new MPS methods into the master branch.

Thanks! This function does more than just inserting Jordan wigner strings: it maps a representation of a term in the form [('N', 1), ('C', 5), ('Cd', 3)] into a form having operators on each site for some range, ['N', 'Id', 'JW Cd', 'JW', 'C'] . Some minor issues: I think it should be for j in range...

Welcome :) The Mixer is designed in such a way that it preserves the symmetries of the Hamiltonian. Basically, it takes the state, calculates the reduced density matrix and applies H to it, and adds this to the original state with a small amplitude. In particular, if you turn on charge conservation ...

Sorry for not getting back directly... 1) The idea is to gradually decrease the time step to reach convergence as quickly as possible. Thanks for pointing out the missing documentation, I've just updated it. There's a subtle thing that even when using a second or fourth order Trotter decomposition i...

I've updated the main repository with quite some changes in the models, so let me comment on this once more. Making the jungle of classes even worse, I've added two more classes: a common base Model and the CouplingMPOModel . Moreover, I've update most of the models in TeNPy to use this new Coupling...

Yes, I understood that, so probably I wasn't clear enough. Let me try again by writing code: U_bond = eng._U[0][i] # this is the np_conserved Array representing exp(-dt* H_bond) on bond (i-1, i) U_bond[0, 0, 0, 0] # the entry corresponding to spin up on each site U_bond_np = U_bond.to_ndarray() # co...

You can just directly read out the coefficients of an np_conserved Array A in the same way as for a numpy Array, for example A[0, 1] will give you a float, if A is a matrix with 2 indices. Even slices are supported, e.g. for A[:, 2] the result will be a np_conserved Array of dimension 1. If you pref...

It takes multiple onsite operators acting on n neighboring sites. To do that with MPS.expecation_value(), you need to generate a full n-site operator before by Hand, which becomes impossible for n > 15. This function contracts in a different order than the MPS.expectation_value(), so it stays "...

It seemed not to be that hard after all, following your suggestions. That's what I hoped :) ... and the results completely disagree with those I got from the simple iTEBD code for the preceding ref. ... And that shouldn't happen :? Do you need to truncate strongly? Is there symmetry breaking? Or a ...

Oh oh :o But there's a simple solution: update your TeNPy source to the newest version with a git pull (and if necessary, compile the np_conserved parts again). Since we are still in the 0.XX version numbers, I allowed myself to do a few backwards incompatible change(s). Moving the "bc" ke...