Extending Quantum Machine Learning Approaches to Black-Hole Information Retrieval: A Theoretical Framework Incorporating Virtual-Particle Coupling and Local Gravity Perturbations

Black-hole information retrieval stands at the intersection of quantum mechanics and general relativity,
posing one of the most profound challenges in modern theoretical physics. Building upon recent
advances in Quantum Machine Learning (QML), which demonstrate that quantum algorithms can
decode information from simulated Hawking radiation, this work introduces a theoretical framework that
extends those models by incorporating virtual particle–antiparticle coupling and local gravitational
perturbations near the event horizon.
A simplified two-qubit interaction Hamiltonian models the coupling between virtual particles, while a
phenomenological relation connects the interaction energy to small perturbations in the spacetime
metric. Using a hybrid QML network, the framework simultaneously performs information decoding and
predicts corresponding gravity-perturbation maps.
Hypothetical results suggest that entanglement entropy correlates with the strength of metric
fluctuations, implying that microscopic virtual-particle processes could leave detectable imprints in
astrophysical observables such as X-ray spectral variability and accretion-disk polarization. This study
establishes a bridge between quantum information theory, machine learning, and black-hole
astrophysics, providing a new pathway for probing the hidden structure of spacetime.