Add all-in-one wrappers.

.gitignore

@@ -68,6 +68,7 @@ instance/
 
 # Sphinx documentation
 docs/_build/
+docs/operator_tools/autogen
 
 # PyBuilder
 target/

Link to examples

@@ -0,0 +1 @@
+/home/kylegulshen/forest-benchmarking/docs/examples

docs/_templates/layout.html

@@ -1,5 +1,4 @@
 {% extends "!layout.html" %}
-{% set script_files = script_files + ["_static/mathjax_macros.js"] %}
 {% block extrahead %}
     {{ super() }}
 {% endblock %}

docs/class_logic.rst

@@ -1,4 +1,5 @@
-.. currentmodule:: forest.benchmarking.classical_logic
+.. module:: forest.benchmarking.classical_logic
+
 Classical Logic
 ===============
 
@@ -7,11 +8,10 @@ The code is contained in the module `classical_logic`.
 
 The benchmark is simplistic and not very rigorous as it does not test any specific feature of the hardware. Further the whole circuit is classical in the sense that we start and end in computational basis states and all gates simply perform classical not, controlled not (`CNOT`), or doubly controlled not (`CCNOT` aka a Toffoli gate). Finally, even for the modest task of adding two one bit numbers, the `CZ` gate (our fundamental two qubit gate) count is very high for the circuit. This in turn implies a low probability of the entire circuit working.
 
-Running DFE
------------
 
-.. todo:: Put an overview or mini tutorial here.
+.. toctree::
 
+    examples/ripple_adder_benchmark
 
 Circuit Primitives
 ------------------
@@ -28,7 +28,6 @@ Circuit Primitives
 
 Ripple Carry adder
 ------------------
-.. currentmodule:: forest.benchmarking.direct_fidelity_estimation
 .. autosummary::
     :toctree: autogen
     :template: autosumm.rst

docs/compilation.rst

@@ -1,20 +1,15 @@
-.. currentmodule:: forest.benchmarking.compilation
+.. module:: forest.benchmarking.compilation
+
 Basic Compilation
 =================
 
-Rigetti's native compiler ``quilc`` is a highly advanced and world class complier. It
+Rigetti's native compiler ``quilc`` is a highly advanced and world class compiler. It
 has many features to optimize the performance of quantum  algorithms.
 
 In QCVV we need to be certain that the circuit we wish to run is the one that is run. For this
-reason we have built the module ``compilation`` it's functionality is rudimentary but easy to
+reason we have built the module ``compilation``.  Its functionality is rudimentary but easy to
 understand.
 
-Overview of Basic Compile
--------------------------
-
-.. todo:: Put an overview or mini tutorial here.
-
-
 Basic Compile
 -------------
 

docs/conf.py

@@ -46,7 +46,7 @@
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.autosummary',
-    'sphinx_autodoc_typehints',
+    # 'sphinx_autodoc_typehints',
     'sphinx.ext.doctest',
     'sphinx.ext.todo',
     'sphinx.ext.intersphinx',
@@ -59,7 +59,7 @@
 autosummary_generate = True
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates/']
+templates_path = ['_templates']
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:

docs/dfe.rst

@@ -1,28 +1,30 @@
+.. module:: forest.benchmarking.direct_fidelity_estimation
+
 Direct Fidelity Estimation
 ==========================
 
-Direct fidelity estimation (DFE) uses knowledge about the target state or process to perform a
-target measurements to quickly certify a quantum state or a quantum process at lower cost than full
-tomography.
+Direct fidelity estimation (DFE) uses knowledge about the ideal target state or process to perform
+a tailored set of measurements to quickly certify a quantum state or a quantum process at lower
+cost than full tomography.
 
+.. toctree::
 
-Running DFE
------------
+    examples/direct_fidelity_estimation
 
-.. todo:: Put an overview or mini tutorial here.
+.. autosummary::
+    :toctree: autogen
+    :template: autosumm.rst
 
+    do_dfe
 
 State DFE
 ---------
-.. currentmodule:: forest.benchmarking.direct_fidelity_estimation
 .. autosummary::
     :toctree: autogen
     :template: autosumm.rst
 
     generate_exhaustive_state_dfe_experiment
     generate_monte_carlo_state_dfe_experiment
-    acquire_dfe_data
-    estimate_dfe
 
 
 Process DFE
@@ -33,5 +35,21 @@ Process DFE
 
     generate_exhaustive_process_dfe_experiment
     generate_monte_carlo_process_dfe_experiment
+
+
+Data Acquisition
+----------------
+.. autosummary::
+    :toctree: autogen
+    :template: autosumm.rst
+
     acquire_dfe_data
-    estimate_dfe
+
+
+Analysis
+--------
+.. autosummary::
+    :toctree: autogen
+    :template: autosumm.rst
+
+    estimate_dfe

docs/dist_meas.rst

@@ -1,4 +1,4 @@
-.. currentmodule:: forest.benchmarking.distance_measures
+.. module:: forest.benchmarking.distance_measures
 
 Distance Measures
 =================
@@ -10,6 +10,9 @@ allows you to explore some quantitative measures of comparing quantum states and
 
 Distance measures between states or processes can be subtle. We recommend thinking about the *operational interpretation* of each measure before using the measure.
 
+.. toctree::
+
+    examples/distance_measures
 
 Distance measures between quantum states
 ----------------------------------------

docs/ent_states.rst

@@ -1,10 +1,13 @@
-.. currentmodule:: forest.benchmarking.entangled_states
+.. module:: forest.benchmarking.entangled_states
 
 Entangled States
 ================
 
 The module ``entangled_states.py`` allows you to simply create graph states GHZ states.
 
+.. toctree::
+
+    examples/entangled_states
 
 .. autosummary::
     :toctree: autogen