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…using both iTEBD and OQuPy
…sor in OQuPy + testing
…of time dependent Hamiltonian to iTEBD-example
… to init from get_mpo.
Modified example to use this capability.
… used with the bath_dynamics.ipynb code.
…tions of dynamics without gradients.
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Hi @emile-cochin! Thank you for submitting this pull request and sorry for the delayed reply. Because several of the changes that need to be made are of a type that is easier for me to quickly carry out rather then explain, I'd like to continue my go-over this code myself before we start code review together. I'll get back to you sometime this week. Best -- gefux |
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Hi @emile-cochin. Thank you for this.
I made some comments with suggestions for the core functionality.
Beside this there are a few other topics, which we'll maybe resolve in another round of review later, but I mention here for completness:
- Create coverage test
- Create physics tests
- Create a tutorial
- Fix code style (linter)
- [Optional] some performance testing
| progress = get_progress(None) | ||
| title = "--> TTI-TEMPO building:" | ||
| with progress(self.n_c, title) as prog_bar: | ||
| for k in range(1, self.n_c +1): |
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I think this should be accessible / interuptable by the user. For all computationally heavy calculations we want allow the user checkpointing, i.e. for a memory cutoff of 200 they should be able to save the python object and environment when they have done 100 steps, and then continue.
This is not a high priority, but I think this backend should be restructured such that there is an compute_step() method, that just computes the next step, and the loop (including the progress bar) should be moved into a TTITempo.compute() method.
| aim = np.concatenate((np.flip(self.eta[:n].imag), np.zeros(n))) | ||
| return np.exp(-np.dot(s_diff_path, np.convolve(are, s_diff_path, 'valid')[:n]) - 1j * np.dot(s_diff_path, np.convolve(aim, s_sum_path, 'valid')[:n])) | ||
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| def evolve(self, h_s: np.ndarray, rho_0: np.ndarray, n: int) -> np.ndarray: |
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We don't need this method, right?
The TTI process tensor should work smoothly with our compute_dynamics() function.
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Indeed, that method is just left from Link's original code and is not needed/used.
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| return rho_t | ||
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| def steadystate(self, h_s: np.ndarray) -> np.ndarray: |
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This I would to also like to see outside of the backend as a function compute_steady_state() in system_dynamics.py using a TTI process tensor and a oqupy.System object.
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I agree but perhaps in another pull request? This is another leftover from Link's original code. If we wanted this functionality in OQuPy I agree it should take an oqupy.System object (and account for e.g. the Liouvillian dissipators).
| return self._dt | ||
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| @property | ||
| def max_step(self) -> Union[int, float]: |
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I am makeing a comment on the Process Tensor classes in the #155 issue thread.
3 participants
Summary
Pull request for implementing time-translationally invariant process tensors (TTI-PTs) into OQuPy, as agreed in #155.
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tox -e py36(to runpytest) the code tests.tox -e style(to runpylint) the code style tests./docs/pages/modules.rsthas been updated./docs/pages/api.rsthas been updated.