Residency demo #1178
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Residency demo #1178
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Co-authored-by: Jorge J. Martínez de Lejarza <61199780+gmlejarza@users.noreply.github.com>
Co-authored-by: serene <150857977+asymptoticgains@users.noreply.github.com>
Co-authored-by: serene <150857977+asymptoticgains@users.noreply.github.com>
Co-authored-by: serene <150857977+asymptoticgains@users.noreply.github.com> Co-authored-by: Jorge J. Martínez de Lejarza <61199780+gmlejarza@users.noreply.github.com>
Refactored the training code and added more explanation to several functions.
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Title: Quantum Circuit Born Machine with Tensor Network Ansätze
Summary:
In this tutorial we employ the NISQ-friendly generative model known as the Quantum Circuit Born Machine (QCBM) introduced in https://arxiv.org/abs/1801.07686 to obtain the probability distribution of the bars and stripes data set. To this end, we use the tensor-network inspired templates available in Pennylane to construct the model's ansatz.
Relevant references:
Possible Drawbacks:
Might have some overlap with this other demo
GOALS — Why are we working on this now?
Showcase the use of built-in PennyLane features within the context of quantum machine learning, particularly for a problem formulated in a paper, with some slight differences.
AUDIENCE — Who is this for?
Quantum Machine Learning researches, and people with intermediate experience in quantum computing
KEYWORDS — What words should be included in the marketing post?
Quantum Circuit Born Machine, Tensor Networks Ansatz, Quantum Machine Learning.
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