Paper: initial draft + actions (#37)

.github/workflows/draft-pdf.yml

@@ -0,0 +1,25 @@
+name: Build JoSS paper
+
+on:
+  workflow_dispatch:
+  push:
+    paths:
+      - 'docs/paper/**'
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          paper-path: docs/paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          path: docs/paper/paper.pdf

docs/paper/paper.bib

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+@misc{Qiskit,
+    author = {{Qiskit contributors}},
+    title = {Qiskit: An Open-source Framework for Quantum Computing},
+    year = {2023},
+    doi = {10.5281/zenodo.2573505}
+}

docs/paper/paper.md

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+---
+title: 'BlackWater: library for solving quantum computing problems using machine learning'
+tags:
+  - quantum computing
+  - qiskit
+  - machine learning
+authors:
+  - name: Iskandar Sitdikov
+    orcid: 0000-0002-6809-8943
+    corresponding: true 
+    affiliation: 1
+affiliations:
+ - name: IBM Quantum, T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
+   index: 1
+date: 22 May 2023
+bibliography: paper.bib
+
+---
+
+# Summary
+
+BlackWater is an open-source machine learning library designed to address 
+the challenges of solving quantum computing problems. By integrating 
+cutting-edge machine learning techniques with the intricacies of quantum 
+computing, BlackWater provides a comprehensive set of tools for researchers 
+and practitioners in the field. This library enables the application of 
+classical machine learning algorithms to quantum computing, bridging 
+the gap between the two domains and accelerating the development of 
+quantum applications.
+
+# Statement of need
+
+The field of quantum computing presents an array of complex challenges, 
+ranging from efficient unitary synthesis to error mitigation and correction. 
+These challenges require innovative approaches to unlock the full 
+potential of quantum computers. Recent advances in machine learning 
+(ML) have showcased their ability to tackle some of the toughest problems 
+across various domains. Recognizing this potential, there is a pressing 
+need to leverage classical ML techniques in the field of quantum computing.
+
+# Acknowledgements
+
+...
+
+# References