Merge pull request #57 from oqc-community/jd/cleanups

Minor clean-ups

README.md

@@ -9,7 +9,7 @@ Some of the key things this approach enables:
 2. Enabling hybrid algorithms to be run on machines and tools with only a gate-level API available. This includes QASM API's if you use its simulation framework.
 3. Lots of optimization potential when passed large amounts of classical context that a quantum algorithm uses to accentuate its own execution.
 
-We also have a [full feature list and quick intro to its concepts](https://github.com/oqc-community/rasqal/blob/develop/features_and_concepts.md) as well as a [draft paper](https://github.com/oqc-community/rasqal/blob/develop/docs/Rasqal%20Draft%20v2.pdf) that covers its internals in excruciating detail.
+We also have a [full feature list and quick intro to its concepts](https://github.com/oqc-community/rasqal/blob/develop/features_and_concepts.md) as well as a [draft paper](https://github.com/oqc-community/rasqal/blob/develop/docs/papers/Rasqal%20Draft%20v2.pdf) that covers its internals in excruciating detail.
 
 If you have any features or ideas you'd like to see implemented feel free to raise a [feature request](https://github.com/oqc-community/Rasqal/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.md&title=)!
 
@@ -19,7 +19,7 @@ If you have any features or ideas you'd like to see implemented feel free to rai
 
 1. Install Rasqal in your favourite Python venv by running `pip install rasqal`. Our current testing is done with `v3.9` of Python.
 2. Read the [quick start](https://github.com/oqc-community/rasqal/blob/develop/examples.md) and look at our [Python example](https://github.com/oqc-community/Rasqal/blob/develop/docs/examples.py).
-3. (Optional) Read the [paper](https://github.com/oqc-community/rasqal/blob/develop/docs/Rasqal%20Draft%20v2.pdf) for a deep-dive into Rasqals concepts and data structures.
+3. (Optional) Read the [paper](https://github.com/oqc-community/rasqal/blob/develop/docs/papers/Rasqal%20Draft%20v2.pdf) for a deep-dive into Rasqals concepts and data structures.
 
 ### Contributing
 

docs/papers/README.md

@@ -0,0 +1,9 @@
+## Papers
+
+This folder containers various papers that cover the different concepts that are on show in Rasqal and can be used for full reference to its ideas.
+
+This is a quick summary of what each contains.
+
+__Rasqal (Draft).__ 
+
+Initial paper explaining foundational concepts and data structures. Covers initial implementation of the graph which powers the systems various functionality as well as the mini-runtime and deferred execution mechanisms.

features_and_concepts.md

@@ -36,7 +36,7 @@ In fact the less the execution run looks like the incoming QIR the better, becau
 
 
 
-If you are interested in a more thorough breakdown of its internals and concepts it has [a paper](https://github.com/oqc-community/rasqal/blob/develop/docs/Rasqal%20Draft%20v2.pdf) which goes into them in detail.
+If you are interested in a more thorough breakdown of its internals and concepts it has [a paper](https://github.com/oqc-community/rasqal/blob/develop/docs/papers/Rasqal%20Draft%20v2.pdf) which goes into them in detail.
 
 ### Features
 

src/rasqal/README.md

@@ -9,7 +9,7 @@ Some of the key things this approach enables:
 2. Enabling hybrid algorithms to be run on machines and tools with only a gate-level API available. This includes QASM API's if you use its simulation framework.
 3. Lots of optimization potential when passed large amounts of classical context that a quantum algorithm uses to accentuate its own execution.
 
-We also have a [full feature list and quick intro to its concepts](https://github.com/oqc-community/rasqal/blob/develop/features_and_concepts.md) as well as a [draft paper](https://github.com/oqc-community/rasqal/blob/develop/docs/Rasqal%20Draft%20v2.pdf) that covers its internals in excruciating detail.
+We also have a [full feature list and quick intro to its concepts](https://github.com/oqc-community/rasqal/blob/develop/features_and_concepts.md) as well as a [draft paper](https://github.com/oqc-community/rasqal/blob/develop/docs/papers/Rasqal%20Draft%20v2.pdf) that covers its internals in excruciating detail.
 
 If you have any features or ideas you'd like to see implemented feel free to raise a [feature request](https://github.com/oqc-community/Rasqal/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.md&title=)!
 
@@ -19,7 +19,7 @@ If you have any features or ideas you'd like to see implemented feel free to rai
 
 1. Install Rasqal in your favourite Python venv by running `pip install rasqal`. Our current testing is done with `v3.9` of Python.
 2. Read the [quick start](https://github.com/oqc-community/rasqal/blob/develop/examples.md) and look at our [Python example](https://github.com/oqc-community/Rasqal/blob/develop/docs/examples.py).
-3. (Optional) Read the [paper](https://github.com/oqc-community/rasqal/blob/develop/docs/Rasqal%20Draft%20v2.pdf) for a deep-dive into Rasqals concepts and data structures.
+3. (Optional) Read the [paper](https://github.com/oqc-community/rasqal/blob/develop/docs/papers/Rasqal%20Draft%20v2.pdf) for a deep-dive into Rasqals concepts and data structures.
 
 ### Contributing
 

src/rasqal/pyproject.toml

@@ -18,7 +18,7 @@ classifiers = [
 ]
 
 authors = [
-    { name = "John Dumbell", email = "euphoricEulogy@gmail.com" }
+    { name = "John Dumbell", email = "john.dumbell42@gmail.com" }
 ]
 
 dependencies = [