Getting Started with Quantum Machine Learning

3.2
15件の評価
提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

Utilize Pennylane.ai as a cross-platform Python library for differentiable programming of quantum computers.

Learn the workflow for developing with Pennylane.ai and build a custom Pennylane.ai Plugin

Convert a Tensorflow Keras network Quantum by layer.

Clock2 hours
Advanced上級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

In this 2-hour long project-based course, you will learn basic principles of how machine learning can benefit from work, and how this can be implemented in Python using the Pennylane library by Xanadu. The Future is Quantum. You've heard the hype. Quantum Computing represents a completely new paradigm in the computing realm, posed to revolutionize entire industries and bring amazing new innovations as they are used for purposes such as material design, pharmaceutical design, genetic and molecular simulations, and weather simulations. The most exciting advancement just may be in the field of Artificial Intelligence and Machine Learning. Quantum computers can theoretically speed up matrix multiplications and process massive amounts of data very quickly, and thus may represent a paradigm shift in AI and ML. Most of this work is yet to be done. That's where you come in. In this project, you will learn how to utilize several software libraries to code quantum algorithms and encode data for use in both classical simulations of quantum devices or actual quantum devices that are available for use over the Internet through vendors such as IBM. I would encourage learners to experiment- How easy is it to add more layers without using frameworks like Tensorflow? What if we add more nodes? What limitations do we come across? The learner is highly encouraged to experiment beyond the scope of the course. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

あなたが開発するスキル

  • Matrix Multiplication
  • Molecular Modelling
  • Differentiable Function
  • Matrices

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  1. Learn the Bare Basics of Quantum Computing and Quantum Machine Learning or QML.

  2. Learn how Pennylane.ai is used and what it does.

  3. Build Qnodes and Customized Templates

  4. Calculating Autograd and Loss Function with Quantum Computing using Pennylane

  5. Developing with the Pennylane.ai API

  6. Building your own Pennylane Plugin

  7. Turning Quantum Nodes into Tensorflow Keras Layers

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

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