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Quantum generative models gain representational power with classical randomness

By PulseAugur Editorial · [1 sources] ·

Researchers have demonstrated that incorporating shared classical randomness into quantum generative models can enable them to represent a broader range of distributions than purely unitary models, even at shallow circuit depths. This finding addresses a long-standing question about whether such randomness provides a provable separation for large systems. The study shows that by adding local Pauli operations controlled by a single random bit to shallow unitary circuits, channel models can generate long-range correlations that are impossible for shallow unitary models with bounded connectivity to reproduce. AI

IMPACT Advances understanding of quantum generative models, potentially influencing future AI research in quantum computing.

RANK_REASON Academic paper detailing a theoretical advance in quantum generative models. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quantum generative models gain representational power with classical randomness

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Academic paper detailing a theoretical advance in quantum generative models. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Arunava Majumder, Marius Krumm, Hendrik Poulsen Nautrup, Hans J. Briegel ·

    Representational separation between unitary and channel quantum generative models via shared classical randomness at shallow depth

    arXiv:2608.05110v1 Announce Type: cross Abstract: Near-term quantum hardware limits circuit depth and often imposes geometrically local connectivity for quantum generative models, restricting the output distributions accessible to shallow unitary Born models. Introducing stochast…

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