## TEBD for time-dependent Hamiltonians

Discussing the best way to implement feature X
abhiroop
Posts: 2
Joined: 06 Aug 2019, 19:34

### TEBD for time-dependent Hamiltonians

Hello,
I am trying to implement a program which calculates the time-dependent Hubbard Hamiltonian with hopping t*exp(-i*phi(t)) if the system is driven by some periodic electric field.

In tenpy/models/fermions_hubbard.py we put t as well as t2 (defined later)

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      for u1, u2, dx in self.lat.nearest_neighbors:
self.add_coupling(t, u1, 'Cdu', u2, 'Cu', dx, 'JW', True)
self.add_coupling(t2, u1, 'Cdu', u2, 'Cu', -dx, 'JW', True)  # t2  has been put in place of t
self.add_coupling(t, u1, 'Cdd', u2, 'Cd', dx, 'JW', True)
self.add_coupling(t2, u1, 'Cdd', u2, 'Cd', -dx, 'JW', True)  # h.c.
self.add_coupling(V, u1, 'Ntot', u2, 'Ntot', dx)

Then, in tenpy/algorithms/tebd.py, in the function tebd.run(), we set Nsteps = 1
Finally, in the main program, we can have a loop for time steps

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    time_steps = 10000   # or any desired number of steps
for tsteps in range(time_steps):
t =cmath.exp(-1.0*math.sin(omega*deltat)*1j)    # here phi(t) = math.sin(omega*deltat) related to my problem, anything can be assigned
t2 =cmath.exp(1.0*math.sin(omega*deltat)*1j)
model_params = dict(L=L, t=t, t2=t2, U=U, V=V, mu=0., bc_MPS='finite', cons_N='N', cons_Sz='Sz')
M = Hubbard_efield_Chain(model_params)
eng = tebd.Engine(psi, M, tebd_params)
eng.run()
psi = eng.psi

I guess this will be useful for simulating time-driven (Floquet type) systems.

Johannes
Site Admin
Posts: 159
Joined: 21 Jul 2018, 12:52
Location: UC Berkeley

### Re: TEBD for time-dependent Hamiltonians

Looks like a good thing.
Actually, if you update your local repository, you'll find that the code for the FermiHubbardModel has been moved to tenpy.models.hubbard.FermiHubbardModel. There it says

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        for u1, u2, dx in self.lat.nearest_neighbors:
self.add_coupling(-t, u1, 'Cdu', u2, 'Cu', dx)
self.add_coupling(-np.conj(t), u2, 'Cdu', u1, 'Cu', -dx)  # h.c.
self.add_coupling(-t, u1, 'Cdd', u2, 'Cd', dx)
self.add_coupling(-np.conj(t), u2, 'Cdd', u1, 'Cd', -dx)  # h.c.
self.add_coupling(V, u1, 'Ntot', u2, 'Ntot', dx)

First of all, note that the sign of the hopping changed, so positive t means the usual $$-t c^\dagger_i c_j + h.c.$$.
Second, note that it uses -np.conj(t), which allows to choose t complex, as in your case. In that way, we don't need to define a second t2 at all, which gives less room for errors.

In general, I planed to provide a way to do such a time dependent simulation for a given, time dependent model paramater (whichever it is), but didn't find time to do it yet. I'd like to base it on a general simulation class, see Issue #46.

Thank you for providing the example how to do that with TEBD, it looks perfectly right.
I'd like to mention that in the tebd_params, you should choose N_steps=1 and order=1 to update the time-dependent Hamiltonian at each small time step. I've never really thought about the correct way to do TEBD with time-dependent Hamiltonians second-order or even fourth-order in the time step. If you have any insight in that direction, please let me know