Data underpinning "Revealing measurement-induced phase transitions by pre-selection"
Authors/Creators
- 1. University of Cologne
Description
We provide the raw data used to produce plots shown in our paper "Revealing measurement-induced phase transitions by pre-selection" and Jupyter Notebooks that where used to plot the data. We also provide code to show simulation details.
Abstract
Pushing forward the understanding of general non-unitary dynamics in controlled quantum platforms has been fueled by the recent discovery of measurement-induced phases and phase transitions. So far, these transitions remained largely elusive, since they are masked in standard quantum mechanical observables due to the randomness of measurement outcomes. Here, we establish a general scheme – pre-selection – to make them observable: The outcome randomness is broken explicitly by steering the system towards a representative state, which corresponds to one out of exponentially many possible measurement outcomes. Remarkably, this steering can be chosen so gently that the basic properties of the underlying measurement-induced transition, such as entanglement structure and critical exponents, are not modified. Pre-selection introduces a unique dark or absorbing state with macroscopic order, replacing the maximally mixed stationary state of the unconditioned measurement trajectory ensemble. This creates a link of measurement-induced phase transitions to new forms of quantum absorbing state transitions, which can be detected by standard means via a local order parameter. This insight further enables a quantum simulation strategy, determining the underlying universality class in state-of-the-art quantum platforms without measurement readout.
Files
Feedbackpaper.zip
Additional details
Related works
- Is metadata for
- Preprint: 10.48550/arXiv.2208.10506 (DOI)
Funding
- Deutsche Forschungsgemeinschaft
- Excellence Strategy Cluster of Excellence Matter and Light for Quantum Computing (ML4Q) EXC 2004/1 390534769
- Deutsche Forschungsgemeinschaft
- Collaborative Research Center (CRC) 183 Project No. 277101999—project B02
- European Research Council
- Horizon 2020 research and innovation program 647434
- Deutsche Forschungsgemeinschaft
- SPP 1929 GiRyd DI 1745/2-1