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README.md

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+# Hybrid quantum-classical unsupervised data clustering based on the Self-Organizing Feature Map
+
+Quantum assisted Self-Organizing Feature Map
+
+Unsupervised machine learning is one of the main techniques employed in artificial intelligence.
+Quantum computers offer opportunities to speed up such machine learning techniques.
+Here, we introduce an algorithm for  quantum assisted unsupervised data clustering using the self-organizing feature map, a type of artificial neural network.
+We make a proof-of-concept realization of one of the central components on the IBM Q Experience
+and show that it allows us to reduce the number of calculations in a number of clusters.
+We compare the results with the classical algorithm on a toy example of unsupervised text clustering.
+
+## Make PDF 
+
+```
+make -C manuscript
+```
+
+## Run notebooks
+
+### Setup environment
+
+```shell
+python3.8 -m venv <path/to/venv>
+source <path/to/venv>/bin/activate
+```
+
+
+### Install dependencies
+
+```shell
+pip install -r requirements.txt
+```
+
+
+### Download necessary data for [NLTK](https://www.nltk.org/install.html) 
+
+```shell
+python -m nltk.downloader punkt 
+python -m nltk.downloader wordnet
+```
+
+
+### Create IPython Kernel
+
+```shell
+pip install ipykernel
+python -m ipykernel install --name qasofm-py3.8 --user
+```
+
+
+### Set proxy configuration for Qiskit if necessary
+
+```shell
+$ cat ~/.qiskit/qiskit-ibm.json
+{
+    "default-ibm-quantum": {
+        "channel": "ibm_quantum",
+        "private_endpoint": false,
+        "proxies": {
+            "urls": {
+                "https": "https://<username>:<password>@<endpoint>"
+            }
+        },
+        "token": "<token>",
+        "url": "https://auth.quantum-computing.ibm.com/api"
+    }
+}
+```