Tutorial

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

Installation

First, we need to install the graphix-perceval library. This can be done using pip:

pip install graphix-perceval

If you have not installed graphix yet, you can install it using pip as well:

pip install graphix

Generating MBQC Pattern

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:

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)

Then we define a circuit to create GHZ state.

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

# plot the pattern
nodes, edges = pattern.get_graph()
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
np.random.seed(100)
nx.draw(g)
plt.show()

Pattern-to-circuit conversion

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

from graphix_perceval import to_perceval
from perceval import pdisplay

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

Running pattern on Perceval simulator

By running the Perceval’s computing backends, We can obtain the probability distribution of the measurement outcomes

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

state	probability
|000>	0.5
|111>	0.5

or sampling distribution with a given number of samples:

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

state	counts
|000>	499
|111>	501

Note

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