.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/move_rotation.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_move_rotation.py: Move and Rotation ================= This example demonstrates moving and rotating the spatial boundary of a logical qubit. .. GENERATED FROM PYTHON SOURCE LINES 6-23 .. code-block:: Python # ruff: noqa: E402 #!/usr/bin/env python # coding: utf-8 # # Move Rotation # # This notebook demonstrates moving and rotating the spatial boundary of a logical # qubit. Rotating the boundary types of a logical qubit is a crucial operation in # lattice surgery. For instance, merging two logical qubits requires their boundary # types to be aligned. Therefore, performing such boundary-type rotations is often # essential to facilitate seamless lattice merging. # ### Construction # # `tqec` provides the builtin function `tqec.gallery.move_rotation` to construct it. .. GENERATED FROM PYTHON SOURCE LINES 24-29 .. code-block:: Python from tqec import Basis from tqec.gallery import move_rotation graph = move_rotation() .. GENERATED FROM PYTHON SOURCE LINES 30-37 .. code-block:: Python graph.view_as_html() # This operation rotates the orientation of the logical observable through a # spatial L-shape junction. As shown below, the correlation surface initially # aligns with the Y-axis and finally aligns with the X-axis. .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 38-41 .. code-block:: Python correlation_surfaces = graph.find_correlation_surfaces() .. GENERATED FROM PYTHON SOURCE LINES 42-52 .. code-block:: Python graph.view_as_html( pop_faces_at_directions=("-Y",), show_correlation_surface=correlation_surfaces[0], ) # ### Example Circuit # # Here we show an example circuit of move rotation with $d=3$ surface code that is initialized and measured in the $X$ basis. You can download the circuit :download:`here <../media/gallery/move_rotation/circuit.stim>` or view it in `Crumble 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# noqa: E501 .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 53-150 .. code-block:: Python from tqec import NoiseModel, compile_block_graph graph = move_rotation(Basis.X) compiled_graph = compile_block_graph(graph) circuit = compiled_graph.generate_stim_circuit( k=1, noise_model=NoiseModel.uniform_depolarizing(p=0.001) ) # ### Simulation # # Here we show the simulation results of both $X$-basis and $Z$-basis # experiments under a **uniform depolarizing** noise model. # #

Click to show the full code used for simulation

# # ```py # from multiprocessing import cpu_count # from pathlib import Path # # import matplotlib.pyplot as plt # import numpy # import sinter # # from tqec.gallery.memory import memory # from tqec.gallery.move_rotation import move_rotation # from tqec import NoiseModel # from tqec.simulation.plotting.inset import plot_observable_as_inset # from tqec.simulation.simulation import start_simulation_using_sinter # from tqec.utils.enums import Basis # # SAVE_DIR = Path("results") # # # def generate_graphs(support_observable_basis: Basis) -> None: # block_graph = move_rotation(support_observable_basis) # zx_graph = block_graph.to_zx_graph() # # correlation_surfaces = block_graph.find_correlation_surfaces() # # stats = start_simulation_using_sinter( # block_graph, # range(1, 4), # list(numpy.logspace(-4, -1, 10)), # NoiseModel.uniform_depolarizing, # manhattan_radius=2, # observables=correlation_surfaces, # num_workers=cpu_count(), # max_shots=1_000_000, # max_errors=5_000, # decoders=["pymatching"], # print_progress=True, # save_resume_filepath=Path( # f"../_examples_database/move_rotation_stats_{support_observable_basis.value}.csv" # ), # database_path=Path("../_examples_database/database.pkl"), # ) # # for i, stat in enumerate(stats): # _, ax = plt.subplots() # sinter.plot_error_rate( # ax=ax, # stats=stat, # x_func=lambda stat: stat.json_metadata["p"], # failure_units_per_shot_func=lambda stat: stat.json_metadata["d"], # group_func=lambda stat: stat.json_metadata["d"], # ) # plot_observable_as_inset(ax, zx_graph, correlation_surfaces[i]) # ax.grid(axis="both") # ax.legend() # ax.loglog() # ax.set_title("Move Rotation Error Rate") # ax.set_xlabel("Physical Error Rate") # ax.set_ylabel("Logical Error Rate(per round)") # fig.savefig( # SAVE_DIR # / f"move_rotation_result_{support_observable_basis}_observable_{i}.png" # ) # # # def main(): # SAVE_DIR.mkdir(exist_ok=True) # generate_graphs(Basis.Z) # generate_graphs(Basis.X) # # # if __name__ == "__main__": # main() # # # ``` # #

# .. GENERATED FROM PYTHON SOURCE LINES 151-214 .. code-block:: Python from multiprocessing import cpu_count from pathlib import Path import matplotlib.pyplot as plt import numpy import sinter from tqec import NoiseModel from tqec.gallery.move_rotation import move_rotation from tqec.simulation.plotting.inset import plot_observable_as_inset from tqec.simulation.simulation import start_simulation_using_sinter from tqec.utils.enums import Basis def generate_graphs(support_observable_basis: Basis) -> None: """Generate the logical error-rate graphs corresponding to the provided basis.""" block_graph = move_rotation(support_observable_basis) zx_graph = block_graph.to_zx_graph() correlation_surfaces = block_graph.find_correlation_surfaces() stats = start_simulation_using_sinter( block_graph, range(1, 4), list(numpy.logspace(-4, -1, 10)), NoiseModel.uniform_depolarizing, manhattan_radius=2, observables=correlation_surfaces, num_workers=cpu_count(), max_shots=1_000_000, max_errors=5_000, decoders=["pymatching"], # note that save_resume_filepath and database_path can help reduce the time taken # by the simulation after the database and result statistics have been saved to # the chosen path save_resume_filepath=Path( f"../_examples_database/move_rotation_stats_{support_observable_basis.value}.csv" ), database_path=Path("../_examples_database/database.pkl"), ) for i, stat in enumerate(stats): _, ax = plt.subplots() sinter.plot_error_rate( ax=ax, stats=stat, x_func=lambda stat: stat.json_metadata["p"], failure_units_per_shot_func=lambda stat: stat.json_metadata["d"], group_func=lambda stat: stat.json_metadata["d"], ) plot_observable_as_inset(ax, zx_graph, correlation_surfaces[i]) ax.grid(axis="both") ax.legend() ax.loglog() ax.set_title("Move Rotation Error Rate") ax.set_xlabel("Physical Error Rate") ax.set_ylabel("Logical Error Rate(per round)") # #### Z Basis .. GENERATED FROM PYTHON SOURCE LINES 215-222 .. code-block:: Python generate_graphs(Basis.Z) # #### X Basis .. image-sg:: /auto_examples/images/sphx_glr_move_rotation_001.png :alt: Move Rotation Error Rate :srcset: /auto_examples/images/sphx_glr_move_rotation_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 223-226 .. code-block:: Python generate_graphs(Basis.X) .. image-sg:: /auto_examples/images/sphx_glr_move_rotation_002.png :alt: Move Rotation Error Rate :srcset: /auto_examples/images/sphx_glr_move_rotation_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 45.910 seconds) .. _sphx_glr_download_auto_examples_move_rotation.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: move_rotation.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: move_rotation.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: move_rotation.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_