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**Title:** Quantum Circuit Born Machines **Summary:** Introduces the ideas of Quantum Circuit Born Machines (QCBMs) along with its gradient-based training. Applies QCBM to learn bars and stripes and two peaks dataset. **Relevant references:** [Differentiable Learning of Quantum Circuit Born Machine](https://arxiv.org/abs/1804.04168) **Possible Drawbacks:** **Related GitHub Issues:** ---- If you are writing a demonstration, please answer these questions to facilitate the marketing process. * GOALS — Why are we working on this now?: The purpose is to use PennyLane to implement a popular algorithm in unsupervised generative modelling based on the paper "Differentiable Learning of Quantum Circuit Born Machine". * AUDIENCE — Who is this for?: The demo provides a gentle introduction to QCBMs, making it suitable for beginners. It also targets individuals interested in generative modelling with quantum algorithms. * KEYWORDS — What words should be included in the marketing post?: QCBM, QML, MMD, Gradient-based Optimization * Which of the following types of documentation is most similar to your file? (more details [here](https://www.notion.so/xanaduai/Different-kinds-of-documentation-69200645fe59442991c71f9e7d8a77f8)) - [ ] Tutorial - [x] Demo - [ ] How-to --------- Co-authored-by: Guillermo Alonso-Linaje <[email protected]> Co-authored-by: Alvaro Ballon <[email protected]>
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