16 lines
1.5 KiB
Markdown
16 lines
1.5 KiB
Markdown
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# Machine Learning (QML)
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- Note: not to be confused with [QML](obsidian://open?vault=Obsidian&file=Coding%20Tips%2FComputers%2FMac%20OS%20X%2FBrowser%2FBrowser%20talk%2FJavascript%2FQML)
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- Quantum machine learning is one of the biggest pillars of quantum computing application that we will see in the workforce of the future
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One of the big contenders in this field is Xanadu.ai's [pennylane](https://pennylane.ai/) software. Recently version 0.25 was just released and a lot more applications can be created.
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- for example this notebook using quantum NLP techniques. Read how to code this on their [blog](https://www.google.com/url?q=https://pennylane.ai/blog/2023/04/quantum-nlp-with-the-lambeq-pennylane-integration/&sa=D&source=docs&ust=1682095330026401&usg=AOvVaw35GtVGGevo_EDtaWpNiiKy)
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- A very good read of [how to put data in quantum machine learning](https://medium.com/mlearning-ai/quantum-data-embedding-algorithms-for-quantum-ai-ml-ag-2-66e7e5e79ae4)
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The following questions can finally be answered with [this](https://pennylane.ai/blog/2022/08/pennylane-v025-released/#new-return-types-for-qnodes-with-multiple-measurements) release
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- [TorchQuantum](https://github.com/mit-han-lab/torchquantum) - PyTorch based framework for machine learning on quantum computers
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- [PaddleQuantum](https://github.com/PaddlePaddle/Quantum) - This is Baidu China's implementation of Quantum Machine Learning
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- [lambeq](https://github.com/CQCL/lambeq) - Python library for Quantum NLP
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