Measurement-induced phase transitions by matrix product states scaling
Guillaume Cecile, Hugo L\'oio, Jacopo De Nardis

TL;DR
This paper investigates measurement-induced phase transitions in quantum spin chains using matrix product states and the TDVP algorithm, revealing a phase transition in the error rate and a charge-sharpening transition separate from the entanglement transition.
Contribution
It demonstrates that TDVP-based MPS evolution can efficiently detect measurement-induced phase transitions and distinguishes charge-sharpening from entanglement transitions in quantum systems.
Findings
Error rate exhibits a phase transition with monitoring strength.
Charge-sharpening transition is distinct from the entanglement transition.
Method effectively determines critical parameters in many-body quantum systems.
Abstract
We study the time evolution of long quantum spin chains subjected to continuous monitoring via matrix product states (MPS) at fixed bond dimension, with the Time-Dependent Variational Principle (TDVP) algorithm. The latter gives an effective classical non-linear evolution with a conserved charge, which approximates the real quantum evolution up to an error. We show that the error rate displays a phase transition in the monitoring strength, which can be well detected by scaling analysis with relatively low values of bond dimensions. The method allows for an efficient numerical determination of the critical measurement-induced phase transition parameters in many-body quantum systems. Moreover, in the presence of U(1) global spin charge, we show the existence of a charge-sharpening transition well separated from the entanglement transition which we detect by studying the charge…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTheoretical and Computational Physics
