qiskit VS Keras

Qiskit

Hello, guys. I am a newbie to quantum computing. I got a question when reading the textbook on Qiskit.org.

Second talk: Gesture Classification on a Smartphone Web-App using a Quantum ComputerDavid Alber and Olaf Hahn will demonstrate that Quantum enhanced Support Vector Machines (QSVMs) can be utilized to classify gestures made by a conventional smartphone. They will showcase how developers can utilize the Qiskit Python framework and provision IBM Cloud and IBM Quantum resources to integrate such models in a traditional application environment seamlessly.Machine learning and quantum are promising technologies with the potential to address yet intractable problems. The hybrid nature of QSVMs makes it possible to deploy such models already today. We will show you how.

Also, see Shor's algorithm for a quantum approach to prime factorization, and maybe have a play with qiskit

Qiskit

I think Python would be a more mainstream choice and so you'll find modules like qiskit or [qutip(https://qutip.org/) already exist and will make life easier.

If you want to be a bit more physics-focused in your coding, it might help to dig up a course or textbook on numerical methods in physics. Being able to numerically solve differential equations is probably the most generally applicable skill in physics. Machine learning methods are pretty ''hot right now'' and might be fun to have a look into. And for quantum technology in particular, you might enjoy having a look at some python packages like Kwant for quantum transport, QuTiP for quantum dynamics and Qiskit for quantum computing. You won't understand the physics for this for quite some time, they might help serve as a bit of inspiration and an indication as to what physicists can use programming for.

Keras

As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.

After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.

All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467

You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?

Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

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I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.