Quantum volume updates

demonstrations/quantum_volume.py

@@ -14,6 +14,11 @@
 
 *Author: Olivia Di Matteo — Posted: 15 December 2020. Last updated: 15 April 2021.*
 
+.. warning::
+ The data in this demo was originally generated using system calibration data from the ``ibmq_lima`` device, 
+ which has since been retired. While the code now uses a fake version of the Lima device, the noise and calibration
+ may have shifted since the data was generated.
+
 Twice per year, a project called the TOP500 [#top500]_ releases a list of the
 500 most powerful supercomputing systems in the world. However, there is a large
 amount of variation in how supercomputers are built. They may run different
@@ -544,29 +549,22 @@ def heavy_output_set(m, probs):
 #
 # A token can be generated by logging into your IBM Q account `here <https://quantum-computing.ibm.com/login>`_ .
 #
-#
 # .. note::
 #
 # Users can get a list of available IBM Q backends by importing IBM Q,
 # specifying their provider and then calling: ``provider.backends()``
 #
-dev_lima = qml.device("qiskit.ibmq", wires=5, backend="ibmq_lima")
+
 
 ##############################################################################
 #
 # First, we can take a look at the arrangement of the qubits on the processor
 # by plotting its hardware graph.
 
-import matplotlib.pyplot as plt
-import networkx as nx
+from rustworkx.visualization import mpl_draw
+from qiskit_ibm_runtime.fake_provider import FakeLimaV2
 
-lima_hardware_graph = nx.Graph(dev_lima.backend.configuration().coupling_map)
-
-nx.draw_networkx(
- lima_hardware_graph,
- node_color="cyan",
- labels={x: x for x in range(dev_lima.num_wires)},
-)
+mpl_draw(FakeLimaV2().coupling_map.graph)
 
 
 ##############################################################################
@@ -586,13 +584,7 @@ def heavy_output_set(m, probs):
 # we'll set up a local device to simulate its behaviour.
 #
 
-from qiskit.providers.aer import noise
-
-noise_model = noise.NoiseModel.from_backend(dev_lima.backend)
-
-dev_noisy = qml.device(
- "qiskit.aer", wires=dev_lima.num_wires, shots=1000, noise_model=noise_model
-)
+dev_noisy = qml.device("qiskit.remote", wires=5, shots=1000, backend=FakeLimaV2())
 
 ##############################################################################
 #
@@ -602,12 +594,10 @@ def heavy_output_set(m, probs):
 # qubit placement and routing techniques [#sabre]_ in order to fit the circuits
 # on the hardware graph in the best way possible.
 
-coupling_map = dev_lima.backend.configuration().to_dict()["coupling_map"]
-
 dev_noisy.set_transpile_args(
  **{
  "optimization_level": 3,
- "coupling_map": coupling_map,
+ "coupling_map": FakeLimaV2().coupling_map,
  "layout_method": "sabre",
  "routing_method": "sabre",
  }
@@ -741,6 +731,7 @@ def heavy_output_set(m, probs):
 # :math:`2\sigma` away from the threshold!
 #
 
+from matplotlib import pyplot as plt
 fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(9, 6))
 ax = ax.ravel()
 
@@ -761,6 +752,7 @@ def heavy_output_set(m, probs):
 fig.suptitle("Heavy output distributions for (simulated) Lima QPU", fontsize=18)
 plt.legend(fontsize=14)
 plt.tight_layout()
+plt.show()
 
 
 ##############################################################################