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Remove link to nonexistent section in the top-level README.md file #1052

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Remove link to nonexistent section in the top-level README.md file #1052

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ac7f2b3
Remove link to nonexistent section
mhucka Apr 17, 2026
50cf704
Merge "Motivation" section into "Features" section
mhucka Apr 17, 2026
b24f242
Accept GCA's suggestion
mhucka Apr 17, 2026
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24 changes: 8 additions & 16 deletions README.md
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Expand Up @@ -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) –
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[Documentation](#documentation) –
[Getting help](#getting-help) –
[Citing TFQ](#citing-tensorflow-quantum) –
[Contact](#contact)
Expand All @@ -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
Expand All @@ -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

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