_index.yaml

# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# ==============================================================================
book_path: /quantum/_book.yaml
project_path: /quantum/_project.yaml
description: >
  A quantum ML library for rapid prototyping of hybrid quantum-classical models.
  Leverage Google’s quantum computing frameworks, all from within TensorFlow.
landing_page:
  custom_css_path: /site-assets/css/style.css
  rows:
  - heading: "TensorFlow Quantum is a library for hybrid quantum-classical machine learning."
    items:
    - classname: devsite-landing-row-50
      description: >
        <p>
        TensorFlow Quantum (TFQ) is a
        <a href='./concepts'>quantum machine learning</a> library for rapid
        prototyping of hybrid quantum-classical ML models. Research in
        quantum algorithms and applications can leverage Google’s quantum
        computing frameworks, all from within TensorFlow.
        </p>
        <p>
        TensorFlow Quantum focuses on <em>quantum data</em> and building
        <em>hybrid quantum-classical models</em>. It integrates quantum computing
        algorithms and logic designed in
        <a href="https://github.com/quantumlib/Cirq" class="external">Cirq</a>,
        and provides
        quantum computing primitives compatible with existing TensorFlow APIs,
        along with high-performance quantum circuit simulators. Read more in the
        <a href="https://arxiv.org/abs/2003.02989" class="external">TensorFlow Quantum white paper</a>.
        </p>
        <p>
        Start with the <a href="./overview">overview</a>, then run the
        <a href="./tutorials/hello_many_worlds">notebook tutorials</a>.
        </p>

      code_block: |
        <pre class = "prettyprint">
        # A hybrid quantum-classical model.
        model = tf.keras.Sequential([
            # Quantum circuit data comes in inside of tensors.
            tf.keras.Input(shape=(), dtype=tf.dtypes.string),

            # Parametrized Quantum Circuit (PQC) provides output
            # data from the input circuits run on a quantum computer.
            tfq.layers.PQC(my_circuit, [cirq.Z(q1), cirq.X(q0)]),

            # Output data from quantum computer passed through model.
            tf.keras.layers.Dense(50)
        ])
        </pre>

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    items:
    - heading: "Power of quantum data"
      path: https://blog.tensorflow.org/2020/11/characterizing-quantum-advantage-in.html
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      buttons:
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          path: https://blog.tensorflow.org/2020/11/characterizing-quantum-advantage-in.html
    - heading: "User Journey: Owen"
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      buttons:
        - label: "Read on TensorFlow blog"
          path: https://blog.tensorflow.org/2020/11/my-experience-with-tensorflow-quantum.html
    - heading: "Announcing<br>TensorFlow Quantum"
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      path: https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html
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        path: https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html
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    - heading: "TensorFlow Quantum (TF Dev Summit '20)"
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      - label: Watch the video
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    - heading: "Programming a quantum computer with Cirq"
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      buttons:
      - label: Watch the video
        path: https://www.youtube.com/watch?v=16ZfkPRVf2w
    - heading: "TensorFlow Quantum on GitHub"
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      path: https://github.com/tensorflow/quantum
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        path: https://github.com/tensorflow/quantum