TeNPy Forum

Welcome! It looks like conda is only looking in the win-32 channel, i.e. for a 32-bit package. However, we've only packaged/compiled a 64-bit version, since virtually all hardware (on which you'd like to run TeNPy) these days has 64-bit CPUs. Is your Windows 64 bit? Then you should also download the...

I'm not too surprised that W_II fails for this. Intuitively, the W_I/W_II methods from the ExpMPOEvolution approximates e^{-iH dt} by taking into account non-overlapping terms of H; it is not designed for a long-range coupling going accross the whole system (which you get by using periodic BC for a ...

Indeed, the tenpy.simulations.Simulation classes are built to support exactly this in a generic way for the various algorithms. The idea is that you start your simulation with `run_simulation`, it saves snapshot at certain checkpoints (e.g. during DMRG between sweeps), and you can resume from these ...

1) You refer to H = ZZ - g_x X, where the spin flip symmetry is a product(X). TeNPy implements the TF Ising model as H = XX - g Z, such that the symmetry becomes product(Z), i.e. the parity of sum(Z) is conserved. This is implemented in tenpy.models.tf_ising.TFIChain , it will by default use conserv...

Just initializing the two TDVP environments independently at the same time will not yield the correct result, since each engine has an MPOEnvironment for the partial contractions of <psi|H|psi>, and the one of engine1 will not get updated during the evolution of engine2. You could call engine1.envir...

You chose an artificial test case of completely decoupled spins; the spin pairs 2-4 and 3-5 will just each propagate together. This is basically a product state, except that 2-4 and 3-5 are entangled. After a single time step with the ExpMPOEvolution, the MPS bond dimension grew to include the Schmi...

Take a look at the irregular lattices; exactly this example can be found in the user guide.
Since the commit b49f0e0 that I just pushed, you can do that by adding the model parameter "irregular_remove".

I'm not sure if I understand what you're doing with which boundary conditions. Indeed, "segment" boundary conditions yield a finite DMRG, because the "segment" is a finite region extracted from an infinite MPS, and you only optimize that region keeping the environments fixed. The...

Whatch out! The model parameters bc_MPS, bc_x, bc_y are only used if you don't specify the lattice instance directly, since the lattice instance already has the boundary conditions! model_params = { "S": 0.5, "conserve": "Sz", "lattice": 'Honeycomb', "Lx&...

What you see here is a symmetry breaking transition; I think it's physic and not a bug in TeNPy. For large Jz, the ground state is an anti-ferromagnet forming an Neel state |up down up down ...> , but by symmetry (product of X on each site), the |down up down up ...> has the same energy. Which one i...

Since I don't own any Apple products, I can hardly test this and have no idea what's required to get it to work. However, the kind folks from conda-forge apparently have a working CI bot system that should help us: see the conda-forge docs If you want support for the M1, you'll have to get active: s...

I think you should just look at the Transfermatrix with the smallest possible number of sites. This would be 2 sites for a Neel state, 1 for a completely translation invariant state. Sadly, we don't have an algorithm for the last case at the moment -even single site DMRG requires a two-site MPS unit...

Hi Pablo, which TeNPy function are you referring to? TransferMatrix. eigenvectors () returns the eigenvalues \lambda of the full transfer matrix, there's no ambiguity there. correlation_length returns the xi in your definition, to make sure it's independent of L; it's basically just changing the uni...

You can use algorithm_params: diag_method: lanczos lanczos_params: E_shift: -10 # choose small enough such that E + E_shift < 0 (also for excitations!) It basically does the same trick as you suggested - it just replaces H -> H + E_shift[inline], but the [inline]E_shift is automatically subtracted a...

When you just call the MPS.__init__() function, it will just assume that it is in right-canonical form. In most cases, people will in some way intialize the MPS from a product state with bond dimension 1 (using MPS.from_product_state or MPS.from_lat_product_state), which is trivially in both left- a...

Hi Leandro, apologies for not replying earlier! Just to check: Did this work out, did you manage it in the end? Or do you still need help? I hope you didn't give up! The t-J model is definitely sufficiently important/generic that it would be a good idea to include it into TeNPy itself. Would you lik...

By default, the option `P_tol_to_trunc` causes DMRG to set the P_tol parameter in the lanczos_params, which causes this warning in case DMRG actually does not use lanczos to diagonalize the effective Hamiltonian (it does exact diagonalizaiton for small enough chi where this is actually faster). I've...

1.) The simplest/naive thing is to just use the usual add_coupling with increasingly longer range interaction |i-j| up to some finite cutoff. Alternatively, the more clever/state of the art way would be to fit the powerlaw by a sum of exponentials, and then use `add_exponentially_decaying_interactio...

Yes, that's the general way to go about this: just define your lattice with the correct number of sites per unit sell, and add the corresponding couplings. Some inspiration might be given by the Hofstadter models in TeNPy. I guess it would also be useful to re-implement the FermionicHubbardModel wit...

Hi Nick, not sure if you already solved this. If you want, you can create a diagonal np_conserved tensor from that with the following snippet, but it is numerically inenfficient to actually do that (because tensordot is more expensive than just scaling an axis, which is what we usually do. # assumin...

What error do you get? Can you please include the error traceback? Otherwise I can only guess what the problem might be.... It seems strange that it works for smaller J_z, but not for larger one. By the way, you can actually do this without implementing a custom lattice/model, just based on the tenp...

Yes, I think you need to recreate the environments from scratch.
Not completely sure, but I think you could try using the old ones as an initial guess for the new environments.

The disentanglers implemented in TeNPy act on two or more consecutive sites in the MPS language, but of course you can try to apply a bunch of them one after the other. It also has code that tries to bring a few sites next to each other and then disentangle them, but in my experience that didn't wor...

You just need to import the file in python, which contains the model definition; then you can just use the "name" of the model in the simulation parameters. To import that file with the definition, you have two options: Either you use the command line option -1 of tenpy-run or python -m te...

To be clear, the actual convergence of DMRG is much faster than just 1/(#sweeps performed), as you see on the right - there's a log-scale on the yaxis