Chaos, Entanglement and Measurement: Field-Theoretic Perspectives on Quantum Information Dynamics

• URL: http://arxiv.org/abs/2512.10484v1
• Date: Thu, 11 Dec 2025 10:04:30 GMT
• Title: Chaos, Entanglement and Measurement: Field-Theoretic Perspectives on Quantum Information Dynamics
• Authors: Anastasiia Tiutiakina,
• Abstract summary: I study scrambling and pseudorandomness in the Brownian Sachdev-Ye-Kitaev (SYK) model.<n>I construct a field theory for weakly measured SYK clusters.<n>I develop a strong-disorder renormalization group for measurement-only SYK clusters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work develops tools to understand how quantum information spreads, scrambles, and is reshaped by measurements in many-body systems. First, I study scrambling and pseudorandomness in the Brownian Sachdev-Ye-Kitaev (SYK) model, quantifying pseudorandomness using unitary k-designs and frame potentials. Using Keldysh path integrals with replicas and disorder averaging, I obtain analytic control of the approach to randomness, identify collective modes that delay convergence to Haar-like behavior, and estimate design times as functions of model parameters, clarifying links between scrambling, complexity growth, and random-circuit phenomenology. Second, I construct a field theory for weakly measured SYK clusters. Starting from a system-ancilla description and a continuum monitoring limit, and using fermionic coherent states with replicas and disorder averaging, I derive a nonlinear sigma model that captures measurement back-action and the competition between interaction-induced scrambling and information extraction, predicting characteristic crossover scales and response signatures that distinguish weak monitoring from fully unitary evolution. Third, I develop a strong-disorder renormalization group for measurement-only SYK clusters, based on the SO(2n) replica algebra and Dasgupta-Ma decimation rules. The flow shows features reminiscent of infinite-randomness behavior, but an order-of-limits subtlety renders the leading recursions non-robust, so the analytic evidence for an infinite-randomness fixed point is inconclusive, even though the average second Renyi entropy displays logarithmic scaling. Together, these results provide a unified language to diagnose when many-body dynamics generate operational randomness and how measurements redirect that flow.

Related papers

• Efficient evaluation of fundamental sensitivity limits and full counting statistics for continuously monitored Gaussian quantum systems [0.0]
  Generalized master equations (GMEs) are convenient tools to compute properties of an open or continuously monitored quantum system.<n>A two-sided master equation yields the fidelity and quantum Fisher information (QFI) of environment states.<n> Tilted master equations provide the full counting statistics of quantum jumps and diffusive measurements.
  arXiv  Detail & Related papers  (2026-02-26T18:09:46Z)
• On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking [49.1352577985191]
  We present a comprehensive analysis of how two-layer neural networks learn features to solve the modular addition task.<n>Our work provides a full mechanistic interpretation of the learned model and a theoretical explanation of its training dynamics.
  arXiv  Detail & Related papers  (2026-02-18T20:25:13Z)
• Entropy-Reservoir Bregman Projection: An Information-Geometric Unification of Model Collapse [3.533187668612022]
  We present EntropyReser Bregman Projection- ERBP, an information-geometric framework that unifies these phenomena.<n>Our theory yields a necessary condition for collapse, (ii) a sufficient condition that guarantees a non-language entropy floor, and (iii) closed-form rates that depend on sample size.
  arXiv  Detail & Related papers  (2025-12-16T19:50:03Z)
• Quench dynamics of the quantum XXZ chain with staggered interactions: Exact results and simulations on digital quantum computers [0.7145429370444325]
  We investigate quench dynamics in the quantum $S=1/2$ XXZ antiferromagnetic chain with staggered and anisotropic interactions in the flat-band limit.<n>We derive exact time-dependent states for arbitrary even system sizes by working in the Bell basis.
  arXiv  Detail & Related papers  (2025-12-03T01:14:54Z)
• Complexity of Quadratic Quantum Chaos [1.3707825707652799]
  We investigate minimal two-body Hamiltonians with random interactions that generate spectra resembling those of Gaussian random matrices.<n>Unlike integrable two-body fermionic systems, the corresponding hard-core boson models exhibit genuinely chaotic dynamics.<n>These minimal models may constitute promising and resource-efficient candidates for probing quantum chaos and information scrambling on near-term quantum devices.
  arXiv  Detail & Related papers  (2025-09-04T10:09:46Z)
• Free Independence and Unitary Design from Random Matrix Product Unitaries [0.0]
  Unitary randomness underpins both fundamental tasks in quantum information and the modern theory of quantum chaos.<n>We study the emergence of freeness in a random matrix unitary ensemble.<n>Local observables are precisely those predicted to thermalize in chaotic many-body systems.
  arXiv  Detail & Related papers  (2025-07-31T18:00:00Z)
• Spatial Reasoning with Denoising Models [49.83744014336816]
  We introduce a framework to perform reasoning over sets of continuous variables via denoising generative models.<n>For the first time, that order of generation can successfully be predicted by the denoising network itself.<n>Using these findings, we can increase the accuracy of specific reasoning tasks from 1% to >50%.
  arXiv  Detail & Related papers  (2025-02-28T14:08:30Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations [114.17826109037048]
  Ordinary Differential Equations (ODEs) have recently gained a lot of attention in machine learning. theoretical aspects, e.g., identifiability and properties of statistical estimation are still obscure. This paper derives a sufficient condition for the identifiability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory.
  arXiv  Detail & Related papers  (2022-10-12T06:46:38Z)
• Unification of Random Dynamical Decoupling and the Quantum Zeno Effect [68.8204255655161]
  We show that the system dynamics under random dynamical decoupling converges to a unitary with a decoupling error that characteristically depends on the convergence speed of the Zeno limit. This reveals a unification of the random dynamical decoupling and the quantum Zeno effect.
  arXiv  Detail & Related papers  (2021-12-08T11:41:38Z)
• Continuous Gaussian Measurements of the Free Boson CFT: A model for Exactly Solvable and Detectable Measurement-Induced Dynamics [0.0]
  We introduce a scenario of measurement-induced many body evolution, which possesses an exact analytical solution: bosonic measurements. We consider an elementary model for quantum criticality, the free boson conformal field theory, and investigate in which way criticality is modified under measurements. For each scenario, we discuss the impact of imperfect measurements, which reduce the purity of the wavefunction and are equivalent to Markovian decoherence.
  arXiv  Detail & Related papers  (2021-08-09T18:00:04Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.