Notebook

Here, we will learn how to use the graphix-perceval library to convert graphix.Pattern objects into perceval.Circuit objects.

We first generate a MBQC pattern using graphix library. We create GHZ state as an example.

First, let us import relevant modules and define function we will use:

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from graphix import Circuit
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx


# define the functions required for GHZ state generation
def ghz(circuit: Circuit):
    """generate GHZ circuit"""
    circuit.h(1)
    circuit.h(2)
    circuit.cnot(0, 1)
    circuit.cnot(0, 2)

# generate the GHZ state generation pattern
circuit = Circuit(3)
ghz(circuit)
pattern = circuit.transpile().pattern

# plot the pattern
pattern.draw_graph()

Pattern-to-circuit conversion¶

Now let us convert the pattern into a circuit using the graphix-perceval library:

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from graphix_perceval import to_perceval
from perceval import pdisplay

exp = to_perceval(pattern)
pdisplay(exp.circ)

By running the Perceval’s computing backends, We can obtain the probability distribution of the measurement outcomes, or sampling distribution with a given number of samples:

In [3]:

# distribution
exp.set_local_processor("SLOS")
dist = exp.get_probability_distribution()
dist.draw()

# sampling
exp.set_local_processor("SLOS")
dist = exp.sample(num_samples=1000)
dist.draw()

Note that the current implementation only supports SLOS and Naive as local Perceval processors. See Perceval documentation for more details.