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Information-Calibrated Quantum Diffusion: Aligning Forward Noise with Reverse Recoverability — arXiv:2608.14083

Source

arXiv (en)

Original

https://arxiv.org/abs/2608.14083

License (as archived)

CC0-1.0

Archived

2026-10-19T04:09:46.068Z

SHA-256 of the archived text

bad2513ecc45b42bc76c93bc0735b438bae8e1dce346c130b7166c6ff41d3c39

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How to cite

Information-Calibrated Quantum Diffusion: Aligning Forward Noise with Reverse Recoverability — arXiv:2608.14083 — arXiv (en), https://arxiv.org/abs/2608.14083 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_215437c19b98 (Metraipolis archive dc_215437c19b98, sha256 bad2513ecc45b42bc76c93bc0735b438bae8e1dce346c130b7166c6ff41d3c39)

@misc{dc215437c19b98,
  title = {Information-Calibrated Quantum Diffusion: Aligning Forward Noise with Reverse Recoverability — arXiv:2608.14083},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14083},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_215437c19b98; sha256 bad2513ecc45b42bc76c93bc0735b438bae8e1dce346c130b7166c6ff41d3c39},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14083. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Quantum diffusion models typically parameterize forward corruption by raw channel strength, even though this parameter does not directly quantify how much ensemble information is erased or how difficult the corresponding reverse problem is. We introduce the classical--quantum information decrement $\Delta_t=I(X{:}Q_{t-1})-I(X{:}Q_t)$ as an intrinsic diffusion coordinate that links forward noise allocation to reverse recoverability. Along depolarization, equalizing $\Delta_t$ yields the unique minimax discretization of the forward information loss, while universal recoverability gives the same quantity an operational interpretation as a physically attainable local recovery budget. We further show that such local calibration is not sufficient for stochastic generation: models can satisfy the same recovery criterion while producing substantially different state distributions. This motivates a stochastic learner that combines information-calibrated recovery constraints with distribution matching. We establish finite-sample calibration and compositional trace-Wasserstein control for the resulting learner. Controlled quantum experiments validate the predicted information--recovery alignment, show that the recovery constraints improve local inversion, and confirm the complementary role of distribution matching in endpoint generation. The resulting framework also achieves stronger endpoint trace-Wasserstein performance than an official QuDDPM implementation with fewer trainable parameters. Overall, our work provides a unified information-theoretic principle for designing forward schedules, calibrating reverse steps, and separating physical recovery from generative coverage in quantum diffusion.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-08-17 in quant-ph).
Categorie arXiv: Quantum Physics (quant-ph).
Pagina del lavoro: https://arxiv.org/abs/2608.14083 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T04:01:17.449Z dalla pagina https://arxiv.org/catchup/quant-ph/2026-08-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).