PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 5, 20260 citationsOpen Access

Universal Scale Equality Between the Intrinsic Entanglement Energy and the Topological Quantum Gravity

View Full Paper
YWYaao Wang

Key Points

  • The aim is to establish a universal identity that connects quantum entanglement with quantum gravity without free parameters.
  • Developed a parameter-free identity that relates intrinsic energy of quantum entanglement to topological quantum gravity.
  • Utilized concepts such as holographic area law, topological entanglement entropy, and Ryu-Takayanagi holography.
  • Employed a K3 surface with B3 symmetry for fixing dimensions.
  • Demonstrated B extLambda_{ ext{E}} = ext{MQG} c^2, showing equality between energy scales.
  • Confirmed that quantum entanglement has a maximum energy limit equivalent to quantum gravity's ultraviolet scale.
  • Removed free parameters, fixing the area-law coefficient solely by topology.

Abstract

We prove a parameter-free, universal identity that unifies quantum information and quantum gravity: the intrinsic energy scale of quantum entanglement \ (₄\) is exactly equal to the topological quantum gravity scale \ (c²\) in SI units. Starting from the holographic area law, topological entanglement entropy, Ryu–Takayanagi holography, and the arithmetic rigidity of a K3 surface with \ (Z₃\) symmetry, we fix the dimensionless area-law coefficient \ (₀\) purely from topology, removing all free parameters. All length, entropy, and field dependence cancels exactly, yielding \ (₄ = c²\). This result shows that the maximum energysustainable by nonlocal quantum entanglement is identical to the fundamental ultraviolet scale of quantum gravity, providing a sharp boundary between quantum information and microscopic spacetime geometry.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Yaao Wang (2026) studied this question.

synapsesocial.com/papers/69d1fde4a79560c99a0a44a4https://doi.org/10.5281/zenodo.19398633
Ask AI
Helpful
Bookmark
Share
View Full Paper