Added a QML folder and a tutorial guidelines file

Quantum Machine Learning/resources.md

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+# Get started with quantum machine learning
+Here are some resources to start your deep dive into the weird and wonderful world of quantum machine learning:
+
+ - **Prerequisites**
+    Explore these before you take the plunge!
+    * [The quantum Fourier transform](https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html)
+    * [Quantum phase estimation](https://qiskit.org/textbook/ch-algorithms/quantum-phase-estimation.html)
+    * [Solving linear systems using the HHL algorithm](https://qiskit.org/textbook/ch-applications/hhl_tutorial.html)
+    * [Grover's algorithm and amplitude amplification](https://qiskit.org/textbook/ch-algorithms/grover.html)
+
+ - **Quantum machine learning tutorials**
+    Build quantum machine learning models by following these guides
+    * [Hello, many worlds: get started with TensorFlow Quantum](https://www.tensorflow.org/quantum/tutorials/hello_many_worlds)
+    * [Distance estimation for k-means clustering](https://towardsdatascience.com/quantum-machine-learning-distance-estimation-for-k-means-clustering-26bccfbfcc76?source=your_stories_page---------------------------)
+    * [Inference on quantum Bayesian networks](https://medium.com/analytics-vidhya/quantum-machine-learning-inference-on-bayesian-networks-351f242816e8?source=your_stories_page---------------------------)
+    * More coming soon!
+
+ - **Research papers**
+    Explore some of the coolest and most important developments in QML. *Warning: might lead to immense frustration and feelings of inadeqaucy.*
+    * [Create superpositions associated with discretized probability distributions](https://arxiv.org/pdf/quant-ph/0208112.pdf)
+    * [The original amplitude amplification and estimation paper](https://arxiv.org/pdf/quant-ph/0005055.pdf)
+    * [Rejection sampling using amplitude amplification](https://arxiv.org/pdf/1402.7359.pdf)
+    * [Learn how to tune quantum circuit parameters](https://arxiv.org/pdf/1803.00745.pdf)
+    * [Quantum neural networks](https://arxiv.org/pdf/1802.06002.pdf)
+    * [Learning discrete probability distributions using hybrid GANs](https://arxiv.org/pdf/1904.00043.pdf)
+    * More coming soon!
+

tutorial_guidelines.md

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+# Tutorial Guidelines
+Please make sure that you can check off  **every** item on this list before submitting your tutorial.
+
+ - [] The tutorial is written as a Jupyter notebook- see [here](https://jupyter.org/) for more.
+ - [] Code or examples are embedded wherever a new concept is introduced.
+ - [] All prerequisites are explicitly stated, with links to articles or websites exploring these prerequisites, or explored in the tutorial.
+ - [] All dependencies are clearly mentioned, with relevant import instructions.
+ - [] Graphics are used to illustrate concepts or examples wherever possible.
+ - [] If the tutorial focuses on writing code towards a certain goal, following along with the tutorial and executing all the code cells in the notebook should give the reader a fully function (if toy) version of the tutorial's focus.
+