Quantum-State Texture Dynamics: Theory and Experiment

• URL: http://arxiv.org/abs/2609.05248v1
• Date: Fri, 04 Sep 2026 15:14:10 GMT
• Title: Quantum-State Texture Dynamics: Theory and Experiment
• Abstract summary: Quantum-state texture (QST) has found applications in several fields from quantum foundations and computation to quantum criticality.<n>Here, we demonstrate that the QST response to an arbitrary finite-dimensional channel is fully encoded in the dual evolution of a single reference state.
• Score: 40.58214278340615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum-state texture (QST) has found applications in several fields from quantum foundations and computation to quantum criticality. However, a general theory of QST dynamics under arbitrary physical processes remains unavailable, limiting both its practical application and experimental exploration. Here, we demonstrate that the QST response to an arbitrary finite-dimensional channel is fully encoded in the dual evolution of a single reference state. This description yields necessary and sufficient conditions for texture preservation and implies exact conservation under all free-unital dynamics. Using a nuclear magnetic resonance quantum processor, we experimentally verify these predictions across distinct channel classes. Furthermore, we show that local QST measurements provide an operational signature of entangling gates in circuit layers. Our results establish quantum-state texture as a resource and a practical diagnostic tool in quantum information processing.

Related papers

• Implementation of Leaking Quantum Walks on a Photonic Processor [0.0]
  We focus on the most promising approach, that is photonic integrated circuits.<n>We present and discuss the results related to absorbing boundaries in QWs obtained through theoretical simulations and experiments conducted with the universal photonic quantum processors realized by Quix Quantum.
  arXiv  Detail & Related papers  (2026-01-19T18:11:12Z)
• Quantum Dynamics with Time-Dependent Neural Quantum States [41.99844472131922]
  We present proof-of-principle time-dependent neural quantum state (NQS) simulations.<n>NQS leverage the parameterization of the wave function with neural-network architectures.
  arXiv  Detail & Related papers  (2025-09-29T14:50:02Z)
• Quanutm-State Texture as a Resource: Measures and Nonclassical Interdependencies [0.3650689241468237]
  Quantum-state texture is a newly recognized quantum resource that has garnered attention with the advancement of quantum theory.<n>We propose two new measures of quantum-state texture and introduce a specific functional form for constructing such measures.<n>We establish connections between quantum-state texture and other prominent quantum resources, such as coherence, imaginarity, and predictability.
  arXiv  Detail & Related papers  (2025-08-10T20:50:11Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Solving reaction dynamics with quantum computing algorithms [42.408991654684876]
  We study quantum algorithms for response functions, relevant for describing different reactions governed by linear response.<n>We focus on nuclear-physics applications and consider a qubit-efficient mapping on the lattice, which can efficiently represent the large volumes required for realistic scattering simulations.
  arXiv  Detail & Related papers  (2024-03-30T00:21:46Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Tomography of Quantum States from Structured Measurements via quantum-aware transformer [11.657645935972463]
  We study the structure of quantum measurements for characterizing a quantum state.<n>We design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices.<n>In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data.
  arXiv  Detail & Related papers  (2023-05-09T13:22:13Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Towards simulating 2D effects in lattice gauge theories on a quantum computer [1.327151508840301]
  We propose an experimental quantum simulation scheme to study ground state properties in two-dimensional quantum electrodynamics (2D QED) using existing quantum technology. The proposal builds on a formulation of lattice gauge theories as effective spin models in arXiv:2006.14160. We present two Variational Quantum Eigensolver (VQE) based protocols for the study of magnetic field effects, and for taking an important first step towards computing the running coupling of QED.
  arXiv  Detail & Related papers  (2020-08-21T01:20:55Z)
• Reconstructing quantum states with quantum reservoir networks [4.724825031148412]
  We introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network, and operates as a comprehensive device for reconstructing an arbitrary quantum state.
  arXiv  Detail & Related papers  (2020-08-14T14:01:55Z)

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.