849 quantum vision transformer

[neogyk]

Quantum Vision Transformer. Paper Implementation

The purpose of this pr is to bring the implementation of Quantum Vision Transformer to classiq community.
The related issue is here

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[TomerGoldfriend]

Thank you @neogyk for this PR. Please provide details about the code so we can review it properly:

• Short introduction, in particular, you shall specify the paper you are implementing including a reference.
• Explain major code blocks, what is implemented and how it is related to the algorithm.
• This looks like a very complex hybrid neural network, please provide its diagram as a figure. I think it will make the notebook clearer.
• Add a snapshot of the quantum part from Classiq visualization.

You can have a look at several examples in our library. It should not be identical in terms of sectioning and wording, but just to understand the rational. Here are some QML examples: https://github.com/Classiq/classiq-library/blob/main/algorithms/qml/hybrid_qnn/hybrid_qnn_for_subset_majority.ipynb, https://github.com/Classiq/classiq-library/blob/main/algorithms/qml/quantum_autoencoder/quantum_autoencoder.ipynb

https://github.com/Classiq/classiq-library/blob/main/algorithms/qml/qgan/qgan_bars_and_strips.ipynb

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[TomerGoldfriend]

Line #15.        def angle_embedding(input_: CArray[CReal, N_QUBITS], qbv: Output[QArray[QBit]]) -> None:

BTW, we have a similar function in our open library that you can use if you want , encode_in_angle (it uses RY instead of RX, and it is working on un-initialized variable, so you do not need to allocate)

https://github.com/Classiq/classiq-library/blob/main/functions/qmod_library_reference/classiq_open_library/variational_data_encoding/variational_data_encoding.ipynb

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[TomerGoldfriend]

Show convergence results etc...

If running is too slow, you can train it once and in the notebook upload a pretrained weights.

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