Getting Started with Quantum Machine Learning

3.2
별점
16개의 평가
제공자:
Coursera Project Network
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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
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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.

개발할 기술

  • Matrix Multiplication
  • Molecular Modelling
  • Differentiable Function
  • Matrices

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

GETTING STARTED WITH QUANTUM MACHINE LEARNING의 최상위 리뷰

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