Notebook

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%matplotlib inline

Visualizing the patterns and flows¶

:class:~graphix.visualization.GraphVisualizer tool offers a wide selection of visualization methods for inspecting the causal structure of the graph associated with the pattern, graph or the (generalized-)flow.

Causal flow¶

First, let us inspect the flow and gflow associated with the resource graph of a pattern. simply call :meth:~graphix.pattern.Pattern.draw_graph method. Below we list the meaning of the node boundary and face colors.

Nodes with red boundaries are the input nodes where the computation starts.
Nodes with gray color is the output nodes where the final state end up in.
Nodes with blue color is the nodes that are measured in Pauli basis, one of X, Y or Z computational bases.
Nodes in white are the ones measured in non-Pauli basis.

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import numpy as np
from graphix import Circuit

circuit = Circuit(3)
circuit.cnot(0, 1)
circuit.cnot(2, 1)
circuit.rx(0, np.pi / 3)
circuit.x(2)
circuit.cnot(2, 1)
pattern = circuit.transpile().pattern
# note that this visualization is not always consistent with the correction set of pattern,
# since we find the correction sets with flow-finding algorithms.
pattern.draw_graph(flow_from_pattern=False, show_measurement_planes=True)

next, show the gflow:

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pattern.perform_pauli_measurements(leave_input=True)
pattern.draw_graph(flow_from_pattern=False, show_measurement_planes=True, node_distance=(1, 0.6))

Correction set ('xflow' and 'zflow' of pattern)¶

next let us visualize the X and Z correction set in the pattern by :code:flow_from_pattern=False statement.

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# node_distance argument specifies the scale of the node arrangement in x and y directions.
pattern.draw_graph(flow_from_pattern=True, show_measurement_planes=True, node_distance=(0.7, 0.6))

Instead of the measurement planes, we can show the local Clifford of the resource graph. see clifford.py for the details of the indices of each single-qubit Clifford operators. 6 is the Hadamard and 8 is the $\sqrt{iY}$ operator.

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pattern.draw_graph(flow_from_pattern=True, show_local_clifford=True, node_distance=(0.7, 0.6))

Visualize based on the graph¶

The visualizer also works without the pattern. Simply supply the

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import networkx as nx

from graphix.visualization import GraphVisualizer

# graph with gflow but no flow
nodes = [1, 2, 3, 4, 5, 6]
edges = [(1, 4), (1, 6), (2, 4), (2, 5), (2, 6), (3, 5), (3, 6)]
inputs = {1, 2, 3}
outputs = {4, 5, 6}
graph = nx.Graph()
graph.add_nodes_from(nodes)
graph.add_edges_from(edges)
meas_planes = {1: "XY", 2: "XY", 3: "XY"}
vis = GraphVisualizer(graph, inputs, outputs, meas_plane=meas_planes)
vis.visualize(show_measurement_planes=True)

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# graph with extended gflow but no flow
nodes = [0, 1, 2, 3, 4, 5]
edges = [(0, 1), (0, 2), (0, 4), (1, 5), (2, 4), (2, 5), (3, 5)]
inputs = {0, 1}
outputs = {4, 5}
graph = nx.Graph()
graph.add_nodes_from(nodes)
graph.add_edges_from(edges)
meas_planes = {0: "XY", 1: "XY", 2: "ZX", 3: "YZ"}
vis = GraphVisualizer(graph, inputs, outputs, meas_plane=meas_planes)
vis.visualize(show_measurement_planes=True)