Getting started

Once you have Graphix installed on your system, let’s run your first measurement-based computation!

In the MBQC model, all computations rely on first preparing an entangled multi-qubit state and then performing a series of local measurements. In Graphix, the resource graph state and the choice of measurements to be performed can be represented using an open graph.

In your terminal or a new python file, type the following:

import networkx as nx
from graphix import OpenGraph, Measurement

og = OpenGraph(
   graph=nx.Graph([(0, 3), (1, 2), (2, 3), (2, 4)]),
   input_nodes=[0, 1],
   output_nodes=[3, 4],
   measurements={node: Measurement.X for node in range(3)}
)

og.draw()
_images/getting_started_0_0.png

To fully describe an MBQC computation, an open graph needs to be supplemented with a correction strategy that assigns conditional Pauli operations to nodes based on previous measurement results. You will learn how to intepret and define correction strategies in later tutorials. For now, let Graphix find one for you and simulate the computation directly.

from numpy.random import default_rng
from graphix import BasicStates

state = og.simulate(input_state=BasicStates.ZERO, rng=default_rng(seed=42))
print(state)

sqrt(2)/2(|00> + |11>)

Congratulations! You’ve just run your first MBQC simulation in Graphix and prepared a Bell state.

To dive deeper into MBQC concepts and learn how to use Graphix to build, optimize and simulate measurement-based quantum programs, head over to the Tutorials section.