TeNPy Forum

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...

All of tenpy (DMRG etc) uses the np_conserved module for tensors, as this is necessary for implementing symmetries. The basic idea is that the `Array` defined there has a very similar interface as numpy arrays, instead of numpy.tensordot(...) you can just use np_conserved.tensordot(...) etc. Our `Ar...

This is exactly the place where you're supposed to ask these questions ;-) I know that the documentation of each function and class is a bit overwhelming in the beginning, and it's hard to get an overview. Some more introductory notes are in the userguide section of the tenpy documentation, for you ...

So this is the state |0...01....1> which has a fixed, well defined particle number L//2, and can be initialized with MPS.from_product_state(). This is a highly non-uniform state, with particles only on the right half. Instead, I would highly recommend to use a more uniform initial state |1010101...>...

I checked explicitly, that the Hamiltonian is correct for Lx=1, Ly=3 by multipliying out the MPO matrices. I get the terms X_0 X_6 X_7 X_{11} + X_2 X_7 X_8 X_9 + X_4 X_9 X_{10} X_{11} + Z_0 Z_1 Z_2 Z_7 + Z_2 Z_3 Z_4 Z_9 + Z_0 Z_4 Z_5 Z_{11} starting in the MPS unit cell of sites 0-5, which is exactl...

What is the value of the "ov" returned by compute_K()? Is your DMRG converged well? Do you maybe expect/have symmetry breaking? An entanglement entropy of 3.3 at some bonds is already quite large, did you choose the bond dimension large enough that you are fairly converged and have accepta...

As a side note for future readers : The question makes more sense if you ask for doing a real-time evolution instead of DMRG. Formally, one can decompose the state as |psi> = \sum_n c_n |psi_n\rangle where |\psi_n\rangle is a state with well defined partice number n. Evolving each of theses states s...

I'm not sure I understand what you want to do. Charge conservation can be used when your Hamiltonian commutes with the (total) particle number operator, which is the case for the usual Bose Hubbard model. In other words, the Hamiltonian is block-diagonal with a block consisting of the subspace with ...