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Quantum Born Machines: Noise Mitigation and Classical Training Methods Explored

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the training of quantum circuit Born machines (QCBMs), a type of quantum generative model. The first paper introduces GENIE, a framework designed to manage hardware noise during QCBM training, showing it can outperform fixed noise levels across various data types. The second paper details a method for classically training conditional IQP Born machines, demonstrating their ability to capture complex conditional distributions and outperform diagonal conditioning on models like the Curie-Weiss spin model. AI

IMPACT Advances in quantum generative models could lead to new approaches for complex data analysis and simulation.

RANK_REASON Two arXiv papers detailing novel methods for training quantum Born machines.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Quantum Born Machines: Noise Mitigation and Classical Training Methods Explored

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Two arXiv papers detailing novel methods for training quantum Born machines.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hoang M. Ngo, Quan Nguyen, Jung Taek Seo, Wanli Xing, My T. Thai ·

    Noise-regularized training of quantum circuit Born machines under a finite shot budget

    arXiv:2611.06630v1 Announce Type: cross Abstract: Quantum circuit Born machines (QCBMs) can learn distributions that are hard to sample from classically, but their value depends on generalizing from finite training data. Here, we study how hardware noise affects this generalizati…

  2. arXiv cs.LG TIER_1 English(EN) · Oriol Ball\'o-Gimbernat, Emiliano Tolotti, Laia Domingo, Fernando Vilari\~no, Adan Garriga ·

    Classical training of conditional instantaneous quantum polynomial-time Born machines

    arXiv:2611.05483v1 Announce Type: cross Abstract: Instantaneous quantum polynomial-time (IQP) Born machines are attractive quantum generative models, as certain IQP circuit families are conjectured to be hard to sample, yet they can be trained classically. Previously proposed con…

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