Equilibration of objective observables in a dynamical model of quantum measurements

• URL: http://arxiv.org/abs/2403.18016v1
• Date: Tue, 26 Mar 2024 18:04:17 GMT
• Title: Equilibration of objective observables in a dynamical model of quantum measurements
• Authors: Sophie Engineer, Tom Rivlin, Sabine Wollmann, Mehul Malik, Maximilian P. E. Lock,
• Abstract summary: This paper builds on the Quantum Darwinism framework derived to explain the emergence of the classical world. We establish a measurement error bound to quantify the probability an observer will obtain an incorrect measurement outcome. Using this error bound, we show that the objectifying observables readily equilibrate on average under the set of Hamiltonians.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The challenge of understanding quantum measurement persists as a fundamental issue in modern physics. Particularly, the abrupt and energy-non-conserving collapse of the wave function appears to contradict classical thermodynamic laws. The contradiction can be resolved by considering measurement itself to be an entropy-increasing process, driven by the second law of thermodynamics. This proposal, dubbed the Measurement-Equilibration Hypothesis, builds on the Quantum Darwinism framework derived to explain the emergence of the classical world. Measurement outcomes thus emerge objectively from unitary dynamics via closed-system equilibration. Working within this framework, we construct the set of \textit{`objectifying observables'} that best encode the measurement statistics of a system in an objective manner, and establish a measurement error bound to quantify the probability an observer will obtain an incorrect measurement outcome. Using this error bound, we show that the objectifying observables readily equilibrate on average under the set of Hamiltonians which preserve the outcome statistics on the measured system. Using a random matrix model for this set, we numerically determine the measurement error bound, finding that the error only approaches zero with increasing environment size when the environment is coarse-grained into so-called observer systems. This indicates the necessity of coarse-graining an environment for the emergence of objective measurement outcomes.

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