qBraid hardware modal (#1333)

Added the qBraid hardware modal to the vqe, intro to qaoa and
variational classifier demos.
Added the qBraid logo to the _static/hardware_logos folder.

Author: CatalinaAlbornoz

_static/hardware_logos/qbraid.png

@@ -29,5 +29,12 @@
             "weight": 1.0
         }
     ],
+    "hardware": [
+        {
+            "id": "qbraid",
+            "link": "https://account.qbraid.com?gitHubUrl=https://github.com/PennyLaneAI/pennylane-demo-notebooks.git",
+            "logo": "/_static/hardware_logos/qbraid.png"
+        }
+    ],
     "discussionForumUrl": "https://discuss.pennylane.ai/t/qaoa-and-optimization/5188"
 }

demonstrations/tutorial_qaoa_intro.metadata.json

@@ -38,7 +38,7 @@
 When considering quantum circuits, it is often convenient to define them by a
 series of quantum gates. But there are many instances where
 it is useful to think of a quantum circuit in terms of a
-`Hamiltonian <https://en.wikipedia.org/wiki/Hamiltonian_(quantum_mechanics)>`__.
+`Hamiltonian <https://pennylane.ai/codebook/hamiltonian-time-evolution/hamiltonians>`__.
 Indeed, gates are physically implemented by performing time evolution under a carefully engineered
 Hamiltonian. These transformations are described by the time evolution operator,
 which is a unitary defined as:"""

demonstrations/tutorial_qaoa_intro.py

@@ -39,5 +39,12 @@
             "weight": 1.0
         }
     ],
+    "hardware": [
+        {
+            "id": "qbraid",
+            "link": "https://account.qbraid.com?gitHubUrl=https://github.com/PennyLaneAI/pennylane-demo-notebooks.git",
+            "logo": "/_static/hardware_logos/qbraid.png"
+        }
+    ],
     "discussionForumUrl": "https://discuss.pennylane.ai/t/variational-classifier-demo-question-on-padding/3367"
 }

demonstrations/tutorial_variational_classifier.metadata.json

@@ -128,7 +128,6 @@ def circuit(weights, x):
 def variational_classifier(weights, bias, x):
     return circuit(weights, x) + bias
 
-
 ##############################################################################
 # Cost
 # ~~~~

demonstrations/tutorial_variational_classifier.py

@@ -73,6 +73,11 @@
             "id": "aws",
             "link": "https://github.com/amazon-braket/amazon-braket-examples/blob/main/examples/pennylane/3_Hydrogen_Molecule_geometry_with_VQE/3_Hydrogen_Molecule_geometry_with_VQE.ipynb",
             "logo": "/_static/hardware_logos/aws.png"
+        },
+        {
+            "id": "qbraid",
+            "link": "https://account.qbraid.com?gitHubUrl=https://github.com/PennyLaneAI/pennylane-demo-notebooks.git",
+            "logo": "/_static/hardware_logos/qbraid.png"
         }
     ],
     "discussionForumUrl": "https://discuss.pennylane.ai/t/a-brief-overview-of-vqe-demo/7333"

demonstrations/tutorial_vqe.metadata.json

@@ -108,7 +108,7 @@
 # where :math:`\theta` is the variational parameter to be optimized in order to find
 # the best approximation to the true ground state. In the Jordan-Wigner [#seeley2012]_ encoding,
 # the first term :math:`|1100\rangle` represents the `Hartree-Fock (HF) state
-# <https://en.wikipedia.org/wiki/Hartree%E2%80%93Fock_method>`_ where the two electrons in
+# <https://pennylane.ai/blog/2022/09/how-to-use-the-hartree-fock-method-in-pennylane>`_ where the two electrons in
 # the molecule occupy the lowest-energy orbitals. The second term :math:`|0011\rangle`
 # encodes a double excitation of the HF state where the two particles are excited from
 # qubits 0, 1 to 2, 3.

demonstrations/tutorial_vqe.py

@@ -10,7 +10,8 @@
       "enum": [
         "aws",
         "covalent",
-        "bluequbit"
+        "bluequbit",
+        "qbraid"
       ]
     },
     "link": {
@@ -26,4 +27,4 @@
   "required": [
       "id", "link", "logo"
   ]
-}
+}