Breakdown of Measurement-Induced Phase Transitions Under Information Loss
- URL: http://arxiv.org/abs/2407.13837v1
- Date: Thu, 18 Jul 2024 18:10:52 GMT
- Title: Breakdown of Measurement-Induced Phase Transitions Under Information Loss
- Authors: Alessio Paviglianiti, Giovanni Di Fresco, Alessandro Silva, Bernardo Spagnolo, Davide Valenti, Angelo Carollo,
- Abstract summary: A quantum-many body system can feature measurement-induced phase transitions (MIPTs)
MIPTs cannot be revealed through ensemble-averaged observables, but it requires the ability to discriminate each trajectory separately.
We explore the fate of MIPTs under an observer's reduced ability to discriminate each measurement outcome.
- Score: 39.36827689390718
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The dynamics of a quantum-many body system subject to measurements is naturally described by an ensemble of quantum trajectories, which can feature measurement-induced phase transitions (MIPTs). This phenomenon cannot be revealed through ensemble-averaged observables, but it requires the ability to discriminate each trajectory separately, making its experimental observation extremely challenging. We explore the fate of MIPTs under an observer's reduced ability to discriminate each measurement outcome. This introduces uncertainty in the state of the system, causing observables to probe a restricted subset of trajectories rather than a single one. By introducing an exactly-solvable Liouvillian model, we examine how long-time spatial correlations are influenced by varying degrees of trajectory averaging. We compute exactly the correlation matrix, Liouvillian gap, and entanglement negativity to demonstrate that averaging over multiple realizations introduces an effective finite lengthscale, beyond which long-range correlations are suppressed. This suggests that partial averaging over trajectories conceals the critical features of individual realizations, thereby blurring away the signatures of distinct measurement-induced phases.
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