Add lattice-utils to dwave-experimental#44
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-move initialization of num_spins to subclass
thisac
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thisac
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Thanks @SebastianGitt! First pass of the lattice submodule.
- I'd recommend running a formatter (black) since all of these files are new and there are a few places with incorrect spacing, etc.
- I think the saving/loading of embeddings can be refined a bit, but I'll have a closer look at it later.
-added more comprehensive docstrings -fixed formatting issues
-added more comprehensive docstrings -fixed formatting issues
…dwave-experimental into add-lattice-utils
-removed spin reversal transform functionality -uses automorphism module already in dwave-experimental -uses data classes for experiment configs
-removed spin reversal transform functionality -uses automorphism module already in dwave-experimental -uses data classes for experiment configs -formatted using black
andrew-d-king
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andrew-d-king
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Looks generally good; I am not able to pore over it in detail but it all seems sensible.
| response_dict = {} | ||
| call_dict = {} | ||
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| for index, param in enumerate(parameter_list): |
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I would like to see the progress bars restored here.
| ret = parameter_list.copy() | ||
| for entry in ret: | ||
| if "anneal_time" in entry: | ||
| entry["anneal_time"] = np.round(entry["anneal_time"], 6) |
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Always nice to see smarter ways of doing things 👍 didn't know np.round took that parameter!
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| return sampler_call | ||
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| def _format_parameter_list( |
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Maybe not for this PR, but ultimately I think that a parameter class should be written, with a format parameter (basically sig figs, for float parameters). It's a bit ugly to do it ad hoc, as I've written it.
| filename = title | ||
| for bad_symbol in "/: ;,": | ||
| filename = filename.replace(bad_symbol, "_") | ||
| fig.savefig(Path(os.getcwd()) / 'figures' / f"{filename}.png") |
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All the figures should be tight_layout(), then saved, then shown. It's inconsistent right now.
| plt.show() | ||
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| # Now we will analyze the kink-kink correlator for the fastest anneals (5ns) | ||
| fig3, ax3 = plt.subplots(1, 2, figsize=(10, 8)) |
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It should be noted that running only five iterations will give poor results for ckk, which is fairly sensitive and requires a converged shim. It also requires more data. But we don't want to put too much QPU usage in a mere example, so I think it is best to just point this out clearly.
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| ax = axes[2, 0] | ||
| ax.plot(cshim[0]) | ||
| ax.set_title(f"Coupler shim, t_a={ANNEAL_TIMES[0]:.3f}μs") |
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| ax.set_title(f"Coupler shim, t_a={ANNEAL_TIMES[0]:.3f}μs") | |
| ax.set_title(f"Coupler shim, $t_a=${ANNEAL_TIMES[0]:.3f}μs") |
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| ax = axes[2, 1] | ||
| ax.plot(cshim[1]) | ||
| ax.set_title(f"Coupler shim, t_a={ANNEAL_TIMES[1]:.3f}μs") |
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| ax.set_title(f"Coupler shim, t_a={ANNEAL_TIMES[1]:.3f}μs") | |
| ax.set_title(f"Coupler shim, $t_a=${ANNEAL_TIMES[1]:.3f}μs") |
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| ax = axes[2, 2] | ||
| ax.plot(cshim[6]) | ||
| ax.set_title(f"Coupler shim, t_a={ANNEAL_TIMES[-1]:.3f}μs") |
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| ax.set_title(f"Coupler shim, t_a={ANNEAL_TIMES[-1]:.3f}μs") | |
| ax.set_title(f"Coupler shim, $t_a=${ANNEAL_TIMES[-1]:.3f}μs") |
| config = experiment.FastAnnealExperimentConfig( | ||
| energy_scale=energy_scale, | ||
| coupler_shim_step=0.05, | ||
| flux_bias_shim_step=1e-6, |
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| flux_bias_shim_step=1e-6, | |
| flux_bias_shim_step=3e-6, |
| # Make parameter list. We will only vary anneal time. | ||
| parameter_list = [{"anneal_time": time} for time in ANNEAL_TIMES] | ||
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| for _ in range(20): #TODO clean this up? |
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| for _ in range(20): #TODO clean this up? | |
| while True: |
3 participants
Add a minimal version of LatQA to dwave-experimental as a submodule named
lattice-utils. Currently supports examples of a 1D Ising Chain and a 2D dimerized triangular lattice. Tests still need to be added to the pull request.