Uncovering measurement-induced entanglement via directional adaptive
dynamics and incomplete information
- URL: http://arxiv.org/abs/2310.01338v1
- Date: Mon, 2 Oct 2023 16:57:50 GMT
- Title: Uncovering measurement-induced entanglement via directional adaptive
dynamics and incomplete information
- Authors: Yu-Xin Wang, Alireza Seif, Aashish A. Clerk
- Abstract summary: rich entanglement dynamics and transitions exhibited by monitored quantum systems typically only exist in the conditional state.
We construct a general recipe for mimicking the conditional entanglement dynamics of a monitored system in a corresponding measurement-free dissipative system.
This mirror setup autonomously implements a measurement-feedforward dynamics that effectively retains a small fraction of the information content in a typical measurement record.
- Score: 23.309064032922507
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rich entanglement dynamics and transitions exhibited by monitored quantum
systems typically only exist in the conditional state, making observation
extremely difficult. In this work we construct a general recipe for mimicking
the conditional entanglement dynamics of a monitored system in a corresponding
measurement-free dissipative system involving directional interactions between
the original system and a set of auxiliary register modes. This mirror setup
autonomously implements a measurement-feedforward dynamics that effectively
retains a small fraction of the information content in a typical measurement
record. We illustrate our ideas in a bosonic system featuring a competition
between entangling measurements and local unitary dynamics, and also discuss
extensions to qubit systems and truly many-body systems.
Related papers
- Learning System Dynamics without Forgetting [60.08612207170659]
Predicting trajectories of systems with unknown dynamics is crucial in various research fields, including physics and biology.
We present a novel framework of Mode-switching Graph ODE (MS-GODE), which can continually learn varying dynamics.
We construct a novel benchmark of biological dynamic systems, featuring diverse systems with disparate dynamics.
arXiv Detail & Related papers (2024-06-30T14:55:18Z) - Interactive System-wise Anomaly Detection [66.3766756452743]
Anomaly detection plays a fundamental role in various applications.
It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data.
We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
arXiv Detail & Related papers (2023-04-21T02:20:24Z) - Graph-informed simulation-based inference for models of active matter [5.533353383316288]
We show that simulation-based inference can be used to robustly infer active matter parameters from system observations.
Our work highlights that high-level system information is contained within the relational structure of a collective system.
arXiv Detail & Related papers (2023-04-05T09:39:17Z) - Statistical Mechanics of Monitored Dissipative Random Circuits [4.0822320577783335]
We study the effects of dissipation on a class of monitored random circuits.
We find that the joint action of monitored measurements and dissipation regimes yields short time, intermediate time and steady state behavior.
arXiv Detail & Related papers (2023-03-14T18:00:18Z) - Evolution of many-body systems under ancilla quantum measurements [58.720142291102135]
We study the concept of implementing quantum measurements by coupling a many-body lattice system to an ancillary degree of freedom.
We find evidence of a disentangling-entangling measurement-induced transition as was previously observed in more abstract models.
arXiv Detail & Related papers (2023-03-13T13:06:40Z) - Learning to Decouple Complex Systems [16.544684282277526]
We propose a sequential learning approach for handling irregularly sampled and cluttered sequential observations.
We argue that the meta-system evolving within a simplex is governed by projected differential equations (ProjDEs)
arXiv Detail & Related papers (2023-02-03T07:24:58Z) - Data-driven Influence Based Clustering of Dynamical Systems [0.0]
Community detection is a challenging and relevant problem in various disciplines of science and engineering.
We propose a novel approach for clustering dynamical systems purely from time-series data.
We illustrate the efficacy of the proposed approach by clustering three different dynamical systems.
arXiv Detail & Related papers (2022-04-05T17:26:47Z) - Many-body entanglement and topology from uncertainties and
measurement-induced modes [0.0]
We present universal characteristics of quantum entanglement and topology through virtual entanglement modes that fluctuate into existence in subsystem measurements.
For generic interacting systems, these modes give rise to a statistical uncertainty which corresponds to entanglement entropies.
In topological systems, the measurement-induced edge modes give rise to quantized and non-analytic uncertainties, providing easily accessible signatures of topology.
arXiv Detail & Related papers (2021-11-30T11:48:07Z) - Tracing Information Flow from Open Quantum Systems [52.77024349608834]
We use photons in a waveguide array to implement a quantum simulation of the coupling of a qubit with a low-dimensional discrete environment.
Using the trace distance between quantum states as a measure of information, we analyze different types of information transfer.
arXiv Detail & Related papers (2021-03-22T16:38:31Z) - Controlling nonlinear dynamical systems into arbitrary states using
machine learning [77.34726150561087]
We propose a novel and fully data driven control scheme which relies on machine learning (ML)
Exploiting recently developed ML-based prediction capabilities of complex systems, we demonstrate that nonlinear systems can be forced to stay in arbitrary dynamical target states coming from any initial state.
Having this highly flexible control scheme with little demands on the amount of required data on hand, we briefly discuss possible applications that range from engineering to medicine.
arXiv Detail & Related papers (2021-02-23T16:58:26Z) - Active Learning for Nonlinear System Identification with Guarantees [102.43355665393067]
We study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs.
We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data.
We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.
arXiv Detail & Related papers (2020-06-18T04:54:11Z)
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.