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This theoretical work proposes a formal link between the black hole information paradox and the double descent phenomenon in quantum machine learning, framing operator reconstruction as a linear inverse problem. By mapping the Page time to an interpolation threshold, the study suggests that fundamental quantum gravity transitions may share mathematical structures with overfitting behaviors in QML models. The analysis relies on spectral properties of Gram matrices derived from Hawking radiation purification to explore this correspondence.
Black hole/quantum machine learning correspondence
arXiv:2506.09678v2 Announce Type: replace Abstract: We explore a possible connection between the black hole information paradox and interpolation geometry underlying the double descent phenomenon in quantum machine learning. State-dependent operator reconstruction on the Hawking radiation can be formulated as a quantum linear inverse problem defined on the black hole-radiation purification. In this picture, the Page time corresponds to the interpolation threshold, where the dimensions of the remaining black hole and the radiation become comparable. Using the Marchenko-Pastur law, we study the spectrum of the corresponding Gram matrices and obtain the variance of the linear reconstruction coefficients. The Page point is then related to both a change in the rank structure of the two subsystems and a strong enhancement of the coefficient variance near the interpolation threshold. This suggests a possible relation between the Page transition and interpolation phenomena in quantum machine learning.
Abstract:We explore a possible connection between the black hole information paradox and interpolation geometry underlying the double descent phenomenon in quantum machine learning. State-dependent operator reconstruction on the Hawking radiation can be formulated as a quantum linear inverse problem defined on the black hole-radiation purification. In this picture, the Page time corresponds to the interpolation threshold, where the dimensions of the remaining black hole and the radiation become comparable. Using the Marchenko-Pastur law, we study the spectrum of the corresponding Gram matrices and obtain the variance of the linear reconstruction coefficients. The Page point is then related to both a change in the rank structure of the two subsystems and a strong enhancement of the coefficient variance near the interpolation threshold. This suggests a possible relation between the Page transition and interpolation phenomena in quantum machine learning.
arXiv:2506.09678 [quant-ph]
(or arXiv:2506.09678v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2506.09678 Focus to learn more arXiv-issued DOI via DataCite
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