PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
November 25, 20250 citationsOpen Access

The Theory of Observerhood in Quantum Measurement: A Trial Formulation of Basis Selection Based on Informational-Geometric Consistency

View Full Paper
YTYamamoto Takayuki

Key Points

  • Measurement is reframed as a structural problem rather than a causal one.
  • Quantum relative entropy quantifies the consistency between the quantum state and observer's structure.
  • The theory addresses decoherence and unifies concepts in relational quantum mechanics.
  • Supports a novel approach to understanding quantum measurement dynamics.

Abstract

Abstract This study proposes a reformulation of the observer’s role in quantum measurement by introducing an informational structural parameter ϕ, referred to as observerhood (SOP). Within this framework, the measurement problem is reframed not as a causal question—“why does collapse occur? ”—but as a structural one: “why does this informational structure stabilize coherently? ” The observer is modeled as an informational agent characterized by an exponential-family state ρ_ϕ defined over a set of measurable operators Aᵢ. The informational consistency between the quantum state ρ and the observer’s structure ϕ is quantified by the quantum relative entropy C (ρ, ϕ) = −S (ρ || ρ_ϕ). The gradient flow of this function yields a non-unitary informational update process of the observer's structure. In a spin-½ system, the stationary distribution μₑq (ϕ) reconstructs the Born rule in the low-informational-temperature limit. While this theory does not claim new empirical predictions, it reconstructs measurement as a dual process of external decoherence and internal consistency, providing a structural foundation that unifies decoherence theory, relational quantum mechanics, and informational structural realism.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Yamamoto Takayuki (2025) studied this question.

synapsesocial.com/papers/692502d187af00ed34ac27bahttps://doi.org/10.5281/zenodo.17583687
Ask AI
Helpful
Bookmark
Share
View Full Paper