Big-data QML university project

Hello everyone,
I am aware that Quantum technology is not advanced to process Big-data. but was wondering if I can do a project in big-data and Machine learning within 2 weeks… can anyone give some nice ideas ?
chatgpt provided me with these topics…

  1. Quantum machine learning for drug discovery: Develop a quantum machine learning algorithm to identify potential drug candidates by analyzing molecular structures and predicting their properties.
  2. Quantum bioinformatics for genome sequencing: Develop a quantum algorithm to analyze and sequence DNA data, which can help improve the accuracy and speed of genome sequencing.
  3. Quantum-enhanced protein folding prediction: Develop a quantum algorithm to predict protein folding and structure, which can help identify disease-causing mutations and improve drug design.
  4. Quantum machine learning for personalized medicine: Develop a quantum machine learning model to analyze medical data and predict personalized treatment plans for patients based on their unique genetic and medical profiles.
  5. Quantum-assisted protein docking: Develop a quantum algorithm to predict the binding affinity between proteins and small molecules, which can help identify potential drug targets and improve drug design.
    what are the implementation possibilities and resources available for this… lemme know if you have any better ideas

Hi @DreamzUpAbove,

Solving these problems is the goal for the future, but they’re probably out of reach for current quantum computers and simulators. It looks like chatgpt got a little bit caught up in the hype :sweat_smile:, especially for a 2-week project.

I can suggest two ideas:
1 - Take a look at the PennyLane demos and find one that you like. Try to run the code and understand the demo. Then try to change the dataset and modify the demo for the new dataset. Finally, look for ways of extending or improving the demo. This will deepen your understanding of the topic in question, and this is something that you can probably do in a couple of weeks.
2 - Another option is taking a paper that has no code implementation and trying to implement it in PennyLane. You will find that this is usually harder than it seems and it will force you to understand the topic of the paper to a high level of detail. If you want to look at some recent papers you can look on the ArXiv or if you want to look at Xanadu papers you can find them in our research page.

Let me know how it goes with these suggestions!

Also, it would be super cool if you published the results of your project as a community demo. Let me know if you have any questions about this!

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