Quantum Neural Network Autoencoder_Quantum Synapse

[Innanov]

Project Name: Quantum Neural Network Autoencoder

Team Name: Quantum Synapse

Which challenges would you like to submit your project for? Hybrid Quantum-Classical Computing Challenge TBD

Project Description: Our proposed project is to create a tutorial on the Quantum Neural Network Autoencoder (QNN-AE), a quantum machine learning algorithm (QML) that has the potential to revolutionize data compression and feature extraction. The tutorial will cover the basic concepts of quantum computing (QC) and neural networks (NN) and how they can be combined to create an autoencoder that leverages the power of quantum mechanics to achieve superior results. The tutorial will also include a brief comparison between classical and quantum autoencoders, highlighting the advantages and limitations of each approach. Besides, it will also include a step-by-step guide on implementing a QNN-AE and provide examples of applications and use cases where QNN-AE has shown promising results.

• Objectives: Introduce the concept of QC, NN, and Quantum Machine Learning. Explain the benefits and challenges of using quantum mechanics in machine learning. Describe the architecture and working of a Quantum Neural Network Autoencoder. Provide a practical implementation guide with code examples. Compare classical and quantum autoencoders, highlighting their strengths and weaknesses. Demonstrate the potential applications and benefits of QNN-AE in real-world scenarios.

• Target audience: The tutorial is aimed at students, researchers, and professionals in ML, QC, and QML interested in learning about QNN-AE and its potential applications.

• Expected outcomes: By the end of the tutorial, participants will clearly understand the fundamental concepts behind Quantum Neural Network Autoencoder and be able to implement them in their projects. They will also gain insight into the potential of quantum computing in machine learning and its implications for future advancements in the field.

Power-Up plan: TBD

Project Repo: https://github.com/Innanov/Quantum-Synapse/tree/981f8ac0235142628d20a0740cd98cf64cddbf78

We allow Xanadu Quantum Technologies to share our email addresses with the Power-Up Sponsors for the purpose of facilitating the delivery of the Power-Ups.

Yes (If applying for AWS’s credits) We have an AWS account

Yes (If applying for IBM’s dedicated slots) We confirm that We have filled in the form with the preferred slots.

Yes (If applying for IBM’s dedicated slots) We have obtained an IBM ID

Yes

[alvaro-at-xanadu]

I cannot access the current link to your repo. Please make sure your repo is accessible (must be public).

[Innanov]

Sorry I fixed it now.