--- a
+++ b/SessionIV_QML/exercise_qml.ipynb
@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise: Quantum machine learning on lower-dimensional single-cell RNAseq data\n",
+    "\n",
+    "We will consider Breast Cancer multi-omics data in this exercise and use it to classify breast cancer subtypes Luminal A and Luminal B. \n",
+    "\n",
+    "We obtained 545 breast cancer samples from TCGA for which both RNAseq and Methylation450 data were available. The dataset consisted of 414 Luminal-A and 141 Luminal-B samples. We considered 28,495 genes and 363,791 methylation sites for a total of 392,286 features. We concatenated the RNAseq and Methylation450 data and projected them to a 10-dimensional space using PCA.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform the experiments from the other notebook and use it to classify Luminal A vs. Luminal B and report the results. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data can be found in the the `../data/BrCa` subdirectory. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#your code here\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}