Merge v0.32 with master

doc/code/qml_measurements.rst

@@ -15,4 +15,4 @@ qml.measurements
     :no-heading:
     :include-all-objects:
     :inheritance-diagram:
-    :skip: MeasurementShapeError, MeasurementValueError, ObservableReturnTypes
+    :skip: MeasurementShapeError, ObservableReturnTypes

doc/code/qml_qchem.rst

@@ -13,7 +13,7 @@ number observables.
 .. automodapi:: pennylane.qchem
     :no-heading:
     :include-all-objects:
-    :skip: taper, symmetry_generators, paulix_ops, import_operator, import_state
+    :skip: taper, symmetry_generators, paulix_ops, import_operator
 
 Differentiable Hartree-Fock
 ---------------------------

doc/development/guide/logging.rst

@@ -12,8 +12,8 @@ To enable logging support in your PennyLane work-flow simply run the following l
    qml.logging.enable_logging()
 
 
-This will ensure all levels of the execution pipeline logs function entries, and
-outputs to handlers pointing to the standard output. Note, since Python's logging framework configuration functions overwrite previous logger configurations, it is expected that :func:`~pennylane.logging.enable_logging()` is called only when not using external logging configurations.
+This will ensure all levels of the execution pipeline log function entries and
+outputs. These are logged to handlers pointing to the standard output. Note, since Python's logging framework configuration functions overwrite previous logger configurations, it is expected that :func:`~pennylane.logging.enable_logging()` is called only when not using external logging configurations.
 
 If you are defining custom logging configurations, you can extend the logging configuration options as defined in the section `Customizing the logging configuration <logging-config>`_, or avoid calling :func:`~pennylane.logging.enable_logging()` in favour of tour custom configuration options.
 

doc/introduction/chemistry.rst

@@ -38,7 +38,7 @@ where:
 The :func:`~.molecular_hamiltonian` function can also be used to construct the molecular Hamiltonian
 with an external backend that uses the
 `OpenFermion-PySCF <https://github.com/quantumlib/OpenFermion-PySCF>`_ plugin interfaced with the
-electronic structure package `PySCF <https://github.com/sunqm/pyscf>`_, which requires separate
+electronic structure package `PySCF <https://github.com/pyscf/pyscf>`_, which requires separate
 installation. This backend is non-differentiable and can be selected by setting
 ``method='pyscf'`` in :func:`~.molecular_hamiltonian`. 
 
@@ -67,8 +67,8 @@ If the electronic Hamiltonian is built independently using
 `OpenFermion <https://github.com/quantumlib/OpenFermion>`_ tools, it can be readily converted 
 to a PennyLane observable using the :func:`~.pennylane.import_operator` function. There is also 
 capability to import wavefunctions (states) that have been pre-computed by traditional quantum chemistry methods
-from `PySCF <https://github.com/sunqm/pyscf>`_, which could be used to for example to provide a better 
-starting point to a quantum algorithm. State import can be accomplished using the :func:`~pennylane.import_state` 
+from `PySCF <https://github.com/pyscf/pyscf>`_, which could be used to for example to provide a better
+starting point to a quantum algorithm. State import can be accomplished using the :func:`~pennylane.qchem.import_state` 
 utility function.
 
 
@@ -264,7 +264,7 @@ Utility functions
     ~pennylane.qchem.givens_decomposition
     ~pennylane.qchem.hf_state
     ~pennylane.import_operator
-    ~pennylane.import_state
+    ~pennylane.qchem.import_state
     ~pennylane.qchem.mol_data
     ~pennylane.qchem.read_structure
 

doc/introduction/data.rst

@@ -26,7 +26,7 @@ These download the desired datasets or load them from local storage if previousl
 
 To specify the dataset to be loaded, the data category (``data_name``) must be
 specified, alongside category-specific keyword arguments. For the full list
-of available datasets, please see the `datasets website <https://pennylane.ai/qml/datasets.html>`_.
+of available datasets, please see the `datasets website <https://pennylane.ai/datasets>`_.
 The :func:`~pennylane.data.load` function returns a ``list`` with the desired data.
 
 >>> H2datasets = qml.data.load("qchem", molname="H2", basis="STO-3G", bondlength=1.1)

doc/introduction/measurements.rst

@@ -309,6 +309,26 @@ Note that we can also specify an outcome when defining a conditional operation:
 >>> qnode_conditional_op_on_zero(*pars)
 tensor([0.88660045, 0.11339955], requires_grad=True)
 
+Wires can be reused as normal after making mid-circuit measurements. Moreover, a measured wire can also be
+reset to the :math:`|0 \rangle` state by setting the ``reset`` keyword argument of :func:`~.pennylane.measure`
+to ``True``.
+
+.. code-block:: python3
+
+    dev = qml.device("default.qubit", wires=3)
+
+    @qml.qnode(dev)
+    def func():
+        qml.PauliX(1)
+        m_0 = qml.measure(1, reset=True)
+        qml.PauliX(1)
+        return qml.probs(wires=[1])
+
+Executing this QNode:
+
+>>> func()
+tensor([0., 1.], requires_grad=True)
+
 The deferred measurement principle provides a natural method to simulate the
 application of mid-circuit measurements and conditional operations in a
 differentiable and device-independent way. Performing true mid-circuit