Quantum Computing Concepts and Implementation in Python

I would like to show you one of my research blog which is recently published on Medium about " Quantum Computing Concepts and Implementation in Python". In this blog I have explained the basics of Quantum computing, how quantum computing principles change our existing neural network performance, basic libraries that we can use to implement the Quantum Neural Network and then I have discussed the implementation of QNN on MNIST dataset.
The blog is available here : Quantum Computing Concepts and Implementation in Python

It is suitable for those who want to learn basics of Quantum computing in depth. Hope it will be helpful for all.

Given the audience here (TensorFlow users), I think your post would have more engagement if you had sample code using TensorFlow Quantum for example

Sure Igsum, I am next working to implement the QNN using Tensorflow Quantum. I have just a Question… How to save and inference Tensorflow Quantum model? Can you please help me in this.

Sorry, I don’t know much about TF Quantum

I’d start from the main documentation:


for save and inference, I’d first try the default Keras API with the save method and the predict method
but again, this is just a guess as I never studied this library myself

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Sure thanks Igsum … I will also try with these documentation first then will see how we can implement in text classification model…