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README.md

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-# 🍒 QuantumFlow: Quantum Tensorflow Machine Learning deployment in K8s
+## ⚛️🔋 QuantumFlow: Quantum Computin Tensorflow in K8s
 
 <br>
 
-## Theoretical Introduction
+#### 👉 this repository contains my work deploying a quantum computing version of tensorflow in kubernets.
+
+<br>
+
+---
+
+### Theoretical Introduction
+
+<br>
 
 * With the recent progress in the development of quantum computing, the development of new quantum ML models could have a profound impact on the world’s biggest problems, leading to breakthroughs in the areas of medicine, materials, sensing, and communications.
 
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 * A key feature of TensorFlow Quantum is the ability to simultaneously train and execute many quantum circuits. This is achieved by TensorFlow’s ability to parallelize computation across a cluster of computers, and the ability to simulate relatively large quantum circuits on multi-core computers. 
 
+<br>
+
 --- 
 
+
+### Steps:
+
 <br>
 
-## Steps:
-
-### Prepare a quantum dataset
+#### Prepare a quantum dataset
 
 - Quantum data is loaded as tensors (a multi-dimensional array of numbers). 
 - Each quantum data tensor is specified as a quantum circuit written in Cirq that generates quantum data on the fly. 
 - The tensor is executed by TensorFlow on the quantum computer to generate a quantum dataset.
 
-### Evaluate a quantum neural network model 
+#### Evaluate a quantum neural network model 
 
 - The researcher can prototype a quantum neural network using Cirq that they will later embed inside of a TensorFlow compute graph. 
 - Parameterized quantum models can be selected from several broad categories based on knowledge of the quantum data's structure. 
 - The goal of the model is to perform quantum processing in order to extract information hidden in a typically entangled state. 
 
-### Sample or Average 
+#### Sample or Average 
 
 - Measurement of quantum states extracts classical information in the form of samples from a classical random variable. 
 - The distribution of values from this random variable generally depends on the quantum state itself and on the measured observable. 
 - As many variational algorithms depend on mean values of measurements, also known as expectation values, TFQ provides methods for averaging over several runs involving steps (1) and (2).
 
-### Evaluate a classical neural networks model 
+#### Evaluate a classical neural networks model 
 
 - Once classical information has been extracted, it is in a format amenable to further classical post-processing.
 - As the extracted information may still be encoded in classical correlations between measured expectations, classical deep neural networks can be applied to distill such correlations.
 
-### Evaluate Cost Function 
+#### Evaluate Cost Function 
 
 - Given the results of classical post-processing, a cost function is evaluated. 
 - This could be based on how accurately the model performs the classification task if the quantum data was labeled, or other criteria if the task is unsupervised.
 
-### Evaluate Gradients & Update Parameters 
+#### Evaluate Gradients & Update Parameters 
 
 - After evaluating the cost function, the free parameters in the pipeline should be updated in a direction expected to decrease the cost.
 - This is most commonly performed via gradient descent.
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 ### Pre-requisites
 
+<br>
+
 * [Vagrant](https://www.vagrantup.com/).
 * [Virtual Box](https://www.virtualbox.org/).
 * [Kubeflow and MiniKF](https://www.kubeflow.org/docs/other-guides/virtual-dev/getting-started-minikf/).
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 ### References
 
+<br>
+
 * [Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning](https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html).