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>

Author: mhucka

README.md

@@ -16,7 +16,6 @@ PyPI](https://img.shields.io/pypi/v/TensorFlow_Quantum.svg?logo=python&logoColor
 [Features](#features) –
 [Installation](#installation) –
 [Quick Start](#quick-start) –
-[Documentation](#documentation) –
 [Getting help](#getting-help) –
 [Citing TFQ](#citing-tensorflow-quantum) –
 [Contact](#contact)
@@ -27,10 +26,9 @@ PyPI](https://img.shields.io/pypi/v/TensorFlow_Quantum.svg?logo=python&logoColor
 
 [TensorFlow Quantum](https://www.tensorflow.org/quantum) (TFQ) is a Python
 framework for hybrid quantum-classical machine learning focused on modeling
-quantum data. It enables quantum algorithms researchers and machine learning
-applications researchers to explore computing workflows that leverage Google’s
-quantum computing offerings – all from within the powerful
-[TensorFlow](https://tensorflow.org) ecosystem.
+quantum data. It provides users with the tools they need to interleave quantum
+algorithms and logic designed in Cirq with the powerful and performant ML tools
+from [TensorFlow](https://tensorflow.org). Here are some of TFQ's features:
 
 *   Integrates with [Cirq](https://github.com/quantumlib/Cirq) for writing
     quantum circuit definitions
@@ -47,17 +45,11 @@ quantum computing offerings – all from within the powerful
 *   Harnesses TensorFlow’s computational machinery to provide exceptional
     performance and scalability
 
-## Motivation
-
-TensorFlow Quantum provides users with the tools they need to interleave quantum
-algorithms and logic designed in Cirq with the powerful and performant ML tools
-from TensorFlow. With this connection, we hope to unlock new and exciting paths
-for quantum computing research that would not have otherwise been possible.
-
-Thanks to its power and scalability, TensorFlow Quantum has already been
-instrumental in enabling ground-breaking research in QML. It empowers
-researchers to pursue questions whose answers can only be obtained through fast
-simulation of many millions of moderately-sized circuits.
+TensorFlow Quantum empowers quantum algorithms and machine learning researchers
+to pursue questions whose answers can only be obtained through fast simulation
+of many millions of moderately-sized circuits. It has already been instrumental
+in enabling ground-breaking research in QML by providing a seamless workflow for
+leveraging Google’s quantum computing offerings.
 
 ## Installation