Update README positioning for researchers

README.md

@@ -34,7 +34,7 @@
 </p>
 
 <p align="center">
-  <strong>Train a quantum computer the same way as a neural network.</strong>
+  The definitive open-source framework for quantum programming. Built by researchers, for research.
   <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/header.png#gh-light-mode-only" width="700px">
     <!--
     Use a relative import for the dark mode image. When loading on PyPI, this
@@ -47,23 +47,23 @@
 
 <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/code.png" width="400px" align="right">
 
-- *Machine learning on quantum hardware*. Connect to quantum hardware using **PyTorch**, **TensorFlow**, **JAX**, **Keras**, or **NumPy**. Build rich and flexible hybrid quantum-classical models.
+- <strong>*Program quantum computers*</strong>. Build quantum circuits with a wide range of state preparations, gates, and measurements. Run on [high-performance simulators](https://pennylane.ai/performance/) or [various hardware devices](https://pennylane.ai/plugins/), with advanced features like mid-circuit measurements and error mitigation.
 
-- *Just in time compilation*. Experimental support for just-in-time
+- <strong>*Master quantum algorithms*</strong>. From NISQ to fault-tolerant quantum computing, unlock algorithms for research and application. Analyze performance, visualize circuits, and access tools for [quantum chemistry](https://docs.pennylane.ai/en/stable/introduction/chemistry.html) and [algorithm development](https://pennylane.ai/search/?contentType=DEMO&categories=algorithms&sort=publication_date).
+
+- <strong>*Machine learning with quantum hardware and simulators*</strong>. Integrate with **PyTorch**, **TensorFlow**, **JAX**, **Keras**, or **NumPy** to define and train hybrid models using quantum-aware optimizers and hardware-compatible gradients for advanced research tasks. [Quantum machine learning quickstart](https://docs.pennylane.ai/en/stable/introduction/interfaces.html).
+
+
+- <strong>*Quantum datasets*</strong>. Access high-quality, pre-simulated datasets to decrease time-to-research and accelerate algorithm development. [Browse the datasets](https://pennylane.ai/datasets/) or contribute your own data.
+
+
+- <strong>*Compilation and performance*</strong>. Experimental support for just-in-time
   compilation. Compile your entire hybrid workflow, with support for 
   advanced features such as adaptive circuits, real-time measurement 
   feedback, and unbounded loops. See
   [Catalyst](https://github.com/pennylaneai/catalyst) for more details.
 
-- *Device-independent*. Run the same quantum circuit on different quantum backends. Install
-  [plugins](https://pennylane.ai/plugins.html) to access even more devices, including **Strawberry
-  Fields**, **Amazon Braket**, **IBM Q**, **Google Cirq**, **Rigetti Forest**, **Qulacs**, **Pasqal**, **Honeywell**, and more.
-
-- *Follow the gradient*. Hardware-friendly **automatic differentiation** of quantum circuits.
-
-- *Batteries included*. Built-in tools for **quantum machine learning**, **optimization**, and
-  **quantum chemistry**. Rapidly prototype using built-in quantum simulators with
-  backpropagation support.
+For more details and additional features, please see the [PennyLane website](https://pennylane.ai/features/).
 
 ## Installation
 
@@ -76,52 +76,55 @@ python -m pip install pennylane
 
 ## Docker support
 
-**Docker** support exists for building using **CPU** and **GPU** (Nvidia CUDA
-11.1+) images. [See a more detailed description
-here](https://pennylane.readthedocs.io/en/stable/development/guide/installation.html#docker).
+Docker images are found on the [PennyLane Docker Hub page](https://hub.docker.com/u/pennylaneai), where there is also a detailed description about PennyLane Docker support. [See description here](https://docs.pennylane.ai/projects/lightning/en/stable/dev/docker.html) for more information.
 
 ## Getting started
 
-For an introduction to quantum machine learning, guides and resources are available on
-PennyLane's [quantum machine learning hub](https://pennylane.ai/qml/):
+Get up and running quickly with PennyLane by following our [quickstart guide](https://docs.pennylane.ai/en/stable/introduction/pennylane.html), designed to introduce key features and help you start building quantum circuits right away.
+
+Whether you're exploring quantum machine learning (QML), quantum computing, or quantum chemistry, PennyLane offers a wide range of tools and resources to support your research:
+
+<img src="./doc/_static/readme/research.png" align="right" width="350px">
 
-<img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/gpu_to_qpu.png" align="right" width="400px">
+### Key Resources:
+
+* [Research-oriented Demos](https://pennylane.ai/qml/demonstrations.html)
+* [Learn Quantum Programming](https://pennylane.ai/qml/) with the [Codebook](https://pennylane.ai/codebook/) and [Coding Challenges](https://pennylane.ai/challenges/)
+* [Frequently Asked Questions](https://pennylane.ai/faq.html)
+* [Glossary](https://pennylane.ai/qml/glossary.html)
+* [Videos](https://pennylane.ai/qml/videos.html)
 
-* [What is quantum machine learning?](https://pennylane.ai/qml/whatisqml)
-* [QML tutorials and demos](https://pennylane.ai/qml/demonstrations)
-* [Frequently asked questions](https://pennylane.ai/faq)
-* [Key concepts of QML](https://pennylane.ai/qml/glossary)
-* [QML videos](https://pennylane.ai/qml/videos)
 
 You can also check out our [documentation](https://pennylane.readthedocs.io) for [quickstart
 guides](https://pennylane.readthedocs.io/en/stable/introduction/pennylane.html) to using PennyLane,
 and detailed developer guides on [how to write your
 own](https://pennylane.readthedocs.io/en/stable/development/plugins.html) PennyLane-compatible
 quantum device.
 
-## Tutorials and demonstrations
+## Demos
 
-Take a deeper dive into quantum machine learning by exploring cutting-edge algorithms on our [demonstrations
-page](https://pennylane.ai/qml/demonstrations).
+Take a deeper dive into quantum computing by exploring cutting-edge algorithms using PennyLane and quantum hardware. [Explore PennyLane demos](https://pennylane.ai/qml/demonstrations.html).
 
 <a href="https://pennylane.ai/qml/demonstrations">
-  <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/demos.png" width="900px">
+  <img src="./doc/_static/readme/demos.png" width="900px">
 </a>
 
-All demonstrations are fully executable, and can be downloaded as Jupyter notebooks and Python
-scripts.
-
 If you would like to contribute your own demo, see our [demo submission
 guide](https://pennylane.ai/qml/demos_submission).
 
-## Videos
+## Research Applications
+
+PennyLane is at the forefront of research in quantum computing, quantum machine learning, and quantum chemistry. Explore how PennyLane is used for research in the following publications:
+
+- **Quantum Computing**: [Fast quantum circuit cutting with randomized measurements](https://quantum-journal.org/papers/q-2023-03-02-934/)
+
+- **Quantum Machine Learning**: [Better than classical? The subtle art of benchmarking quantum machine learning models](https://arxiv.org/abs/2403.07059)
+
+- **Quantum Chemistry**: [Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization](https://quantum-journal.org/papers/q-2024-06-13-1371/)
+
+Impactful research drives PennyLane. Let us know what features you need for your research on [GitHub](https://github.com/PennyLaneAI/pennylane/issues/new?assignees=&labels=enhancement+%3Asparkles%3A&projects=&template=feature_request.yml) or on our [website](https://pennylane.ai/research).
 
-Seeing is believing! Check out [our videos](https://pennylane.ai/qml/videos) to learn about
-PennyLane, quantum computing concepts, and more. 
 
-<a href="https://pennylane.ai/qml/videos">
-  <img src="https://raw.githubusercontent.com/PennyLaneAI/pennylane/master/doc/_static/readme/videos.png" width="900px">
-</a>
 
 ## Contributing to PennyLane
 
@@ -136,7 +139,7 @@ projects or applications built on PennyLane.
 
 See our [contributions
 page](https://github.com/PennyLaneAI/pennylane/blob/master/.github/CONTRIBUTING.md) and our
-[developer hub](https://pennylane.readthedocs.io/en/stable/development/guide.html) for more
+[Development guide](https://pennylane.readthedocs.io/en/stable/development/guide.html) for more
 details.
 
 ## Support
@@ -146,8 +149,7 @@ details.
 
 If you are having issues, please let us know by posting the issue on our GitHub issue tracker.
 
-We also have a [PennyLane discussion forum](https://discuss.pennylane.ai)—come join the community
-and chat with the PennyLane team.
+Join the [PennyLane Discussion Forum](https://discuss.pennylane.ai/) to connect with the quantum community, get support, and engage directly with our team. It’s the perfect place to share ideas, ask questions, and collaborate with fellow researchers and developers!
 
 Note that we are committed to providing a friendly, safe, and welcoming environment for all.
 Please read and respect the [Code of Conduct](.github/CODE_OF_CONDUCT.md).