Update README.md

README.md

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-* Machine learning (ML), while it doesn’t exactly simulate systems in nature, has the ability to learn a model of a system and predict the system’s behavior. O
 
 * 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.
 
-* TFQ provides the tools necessary for bringing the quantum computing and machine learning research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50 - 100 qubits.
+* TFQ provides the tools for bringing the quantum computing and machine learning research together to control and model natural or artificial quantum systems; e.g. (NISQ) processors with ~50 - 100 qubits.
 
 * TFQ integrates Cirq with TensorFlow, and offers high-level abstractions for the design and implementation of both discriminative and generative quantum-classical models by providing quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators.
 
-* A quantum model has the ability to represent and generalize data with a quantum mechanical origin. 
+* A quantum model has the ability to represent and generalize data with a quantum mechanical origin:
+    - Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store.
+    - Quantum data generated by NISQ processors are noisy and are typically entangled just before the measurement occurs. However, applying quantum machine learning to noisy entangled quantum data can maximize extraction of useful classical information. 
 
-* Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store.
-
-* Quantum data generated by NISQ processors are noisy and are typically entangled just before the measurement occurs. However, applying quantum machine learning to noisy entangled quantum data can maximize extraction of useful classical information. 
-
-* Because near-term quantum processors are still fairly small and noisy, quantum models cannot use quantum processors alone — NISQ processors will need to work in concert with classical processors to become effective. As TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is a natural platform for experimenting with hybrid quantum-classical algorithms.
 
 * TFQ contains the basic structures, such as qubits, gates, circuits, and measurement operators that are required for specifying quantum computations. User-specified quantum computations can then be executed in simulation or on real hardware.
 
 * TFQ allows researchers to construct quantum datasets, quantum models, and classical control parameters as tensors in a single computational graph. The outcome of quantum measurements, leading to classical probabilistic events, is obtained by TensorFlow Ops. Training can be done using standard Keras functions.
 
-* Steps:
+--- 
+### Steps:
+
 - 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 - 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. In other words, the quantum model essentially disentangles the input quantum data, leaving the hidden information encoded in classical correlations, thus making it accessible to local measurements and classical post-processing.
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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. 
 
 
-
+-----
-### Pre-requisites
-
-* [Vagrant](https://www.vagrantup.com/).
-* [Virtual Box](https://www.virtualbox.org/).
-* [MiniKF](https://www.kubeflow.org/docs/other-guides/virtual-dev/getting-started-minikf/).
-
-
-
-
-
-### References
-
-* [Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning](https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html).
-
-
 
 ### Pre-requisites
 
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 * [MiniKF](https://www.kubeflow.org/docs/other-guides/virtual-dev/getting-started-minikf/).
 * [TensorFlow Quantum](https://github.com/tensorflow/quantum/blob/master/docs/install.md).
 
+----
 
 ### References