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Remove link to nonexistent section in the top-level README.md file (#1052)
The TOC at the top of the file referred to a _Documentation_ section, but there's actually no such currently in the README file. In addition, there was a _Motivation_ section that was not in the TOC. This PR does a little bit of cleanup by: - Removing the TOC entry for Documentation - Merging the Motivation section into the Features section and removing a little bit of redundancy in the text --------- Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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README.md

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@@ -16,7 +16,6 @@ PyPI](https://img.shields.io/pypi/v/TensorFlow_Quantum.svg?logo=python&logoColor
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[Features](#features) &ndash;
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[Installation](#installation) &ndash;
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[Quick Start](#quick-start) &ndash;
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[Documentation](#documentation) &ndash;
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[Getting help](#getting-help) &ndash;
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[Citing TFQ](#citing-tensorflow-quantum) &ndash;
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[Contact](#contact)
@@ -27,10 +26,9 @@ PyPI](https://img.shields.io/pypi/v/TensorFlow_Quantum.svg?logo=python&logoColor
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[TensorFlow Quantum](https://www.tensorflow.org/quantum) (TFQ) is a Python
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framework for hybrid quantum-classical machine learning focused on modeling
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quantum data. It enables quantum algorithms researchers and machine learning
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applications researchers to explore computing workflows that leverage Google’s
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quantum computing offerings – all from within the powerful
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[TensorFlow](https://tensorflow.org) ecosystem.
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quantum data. It provides users with the tools they need to interleave quantum
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algorithms and logic designed in Cirq with the powerful and performant ML tools
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from [TensorFlow](https://tensorflow.org). Here are some of TFQ's features:
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* Integrates with [Cirq](https://github.com/quantumlib/Cirq) for writing
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quantum circuit definitions
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* Harnesses TensorFlow’s computational machinery to provide exceptional
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performance and scalability
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## Motivation
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TensorFlow Quantum provides users with the tools they need to interleave quantum
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algorithms and logic designed in Cirq with the powerful and performant ML tools
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from TensorFlow. With this connection, we hope to unlock new and exciting paths
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for quantum computing research that would not have otherwise been possible.
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Thanks to its power and scalability, TensorFlow Quantum has already been
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instrumental in enabling ground-breaking research in QML. It empowers
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researchers to pursue questions whose answers can only be obtained through fast
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simulation of many millions of moderately-sized circuits.
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TensorFlow Quantum empowers quantum algorithms and machine learning researchers
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to pursue questions whose answers can only be obtained through fast simulation
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of many millions of moderately-sized circuits. It has already been instrumental
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in enabling ground-breaking research in QML by providing a seamless workflow for
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leveraging Google’s quantum computing offerings.
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## Installation
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