Residency demo

[EmilianoG-byte]

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:

1. https://arxiv.org/abs/1801.07686>
2. http://dx.doi.org/10.1103/PhysRevB.99.155131
3. http://dx.doi.org/10.1103/PhysRevB.85.165146
4. https://arxiv.org/abs/2401.10330
5. http://dx.doi.org/10.1038/nature23458>
6. http://www.deeplearningbook.org
7. http://dx.doi.org/10.1103/PhysRevX.8.031012

Possible Drawbacks:
Might have some overlap with this other demo

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• 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.

• Which of the following types of documentation is most similar to your file?
  (more details here)

• Tutorial
• Demo
• How-to