A nonconvex entanglement monotone determining the characteristic length of entanglement distribution in continuous-variable quantum networks

• URL: http://arxiv.org/abs/2410.12385v2
• Date: Sun, 13 Apr 2025 06:25:12 GMT
• Title: A nonconvex entanglement monotone determining the characteristic length of entanglement distribution in continuous-variable quantum networks
• Authors: Yaqi Zhao, Jinchuan Hou, Kan He, Nicolò Lo Piparo, Xiangyi Meng,
• Abstract summary: Quantum networks (QNs) promise to enhance the performance of various quantum technologies in the near future by distributing entangled states over long distances.<n>Here, we analyze the exponential decay of CV QNs on a chain of pure Gaussian states.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum networks (QNs) promise to enhance the performance of various quantum technologies in the near future by distributing entangled states over long distances. The first step towards this is to develop novel entanglement measures that are both informative and computationally tractable at large scales. While numerous such entanglement measures exist for discrete-variable (DV) systems, a comprehensive exploration for experimentally preferred continuous-variable (CV) systems is lacking. Here, we introduce a class of CV entanglement measures, among which we identify a nonconvex entanglement monotone -- the ratio negativity, which possesses a simple, scalable form that determines the exponential decay of optimal entanglement swapping on a chain of pure Gaussian states. This characterization opens avenues for leveraging statistical physics tools to analyze swapping-protocol-based CV QNs.

Related papers

• Detecting genuine non-Gaussian entanglement [0.0]
  Non-Gaussian entanglement is a central challenge for advancing quantum information processing, photonic quantum computing, and metrology. Here, we put forward continuous-variable counterparts of the recently introduced entanglement criteria based on moments of the partially transposed state. Our multicopy method enables the detection of genuine non-Gaussian entanglement for various relevant state families overlooked by standard approaches.
  arXiv  Detail & Related papers  (2025-04-22T12:22:30Z)
• Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems [44.598178952679575]
  We propose the transformer density operator ansatz for determining the steady states of dissipative quantum many-body systems. We demonstrate the effectiveness of our approach by numerically calculating the steady states of dissipative Ising and Heisenberg spin chain models.
  arXiv  Detail & Related papers  (2025-02-28T05:12:43Z)
• Collective quantum enhancement in critical quantum sensing [37.69303106863453]
  Critical quantum sensing (CQS) protocols can be realized using finite-component phase transitions. We show that a collective quantum advantage can be achieved in a multipartite CQS protocol using a chain of parametrically coupled critical resonators.
  arXiv  Detail & Related papers  (2024-07-25T14:08:39Z)
• Machine-learning-inspired quantum control in many-body dynamics [6.817811305553492]
  We introduce a promising and versatile control neural network tailored to optimize control fields. We address the problem of suppressing defect density and enhancing cat-state fidelity during the passage across the critical point in the quantum Ising model. In comparison to gradient-based power-law quench methods, our approach demonstrates significant advantages for both small system sizes and long-term evolutions.
  arXiv  Detail & Related papers  (2024-04-09T01:47:55Z)
• First-Order Phase Transition of the Schwinger Model with a Quantum Computer [0.0]
  We explore the first-order phase transition in the lattice Schwinger model in the presence of a topological $theta$-term. We show that the electric field density and particle number, observables which reveal the phase structure of the model, can be reliably obtained from the quantum hardware.
  arXiv  Detail & Related papers  (2023-12-20T08:27:49Z)
• Variational Quantum Linear Solver enhanced Quantum Support Vector Machine [3.206157921187139]
  We propose a novel approach called the Variational Quantum Linear solver (VQLS) enhanced QSVM. This is built upon our idea of utilizing the variational quantum linear solver to solve system of linear equations of a least squares-SVM on a NISQ device. The implementation of our approach is evaluated by an extensive series of numerical experiments with the Iris dataset.
  arXiv  Detail & Related papers  (2023-09-14T14:59:58Z)
• Harnessing high-dimensional temporal entanglement using limited interferometric setups [41.94295877935867]
  We develop the first complete analysis of high-dimensional entanglement in the polarization-time-domain. We show how to efficiently certify relevant density matrix elements and security parameters for Quantum Key Distribution. We propose a novel setup that can further enhance the noise resistance of free-space quantum communication.
  arXiv  Detail & Related papers  (2023-08-08T17:44:43Z)
• Machine learning in and out of equilibrium [58.88325379746631]
  Our study uses a Fokker-Planck approach, adapted from statistical physics, to explore these parallels. We focus in particular on the stationary state of the system in the long-time limit, which in conventional SGD is out of equilibrium. We propose a new variation of Langevin dynamics (SGLD) that harnesses without replacement minibatching.
  arXiv  Detail & Related papers  (2023-06-06T09:12:49Z)
• Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution [44.99833362998488]
  We analyze the accuracy and sample complexity of variational Monte Carlo approaches to simulate quantum systems classically. We prove that the most used scheme, the time-dependent Variational Monte Carlo (tVMC), is affected by a systematic statistical bias. We show that a different scheme based on the solution of an optimization problem at each time step is free from such problems.
  arXiv  Detail & Related papers  (2023-05-23T17:38:10Z)
• A Dynamical System View of Langevin-Based Non-Convex Sampling [44.002384711340966]
  Non- sampling is a key challenge in machine learning, central to non-rate optimization in deep learning as well as to approximate its significance. Existing guarantees typically only hold for the averaged distances rather than the more desirable last-rate iterates. We develop a new framework that lifts the above issues by harnessing several tools from the theory systems.
  arXiv  Detail & Related papers  (2022-10-25T09:43:36Z)
• Beyond the Edge of Stability via Two-step Gradient Updates [49.03389279816152]
  Gradient Descent (GD) is a powerful workhorse of modern machine learning. GD's ability to find local minimisers is only guaranteed for losses with Lipschitz gradients. This work focuses on simple, yet representative, learning problems via analysis of two-step gradient updates.
  arXiv  Detail & Related papers  (2022-06-08T21:32:50Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning [96.75889543560497]
  In many real-world problems, collecting a large number of labeled samples is infeasible. Few-shot learning is the dominant approach to address this issue, where the objective is to quickly adapt to novel categories in presence of a limited number of samples. We propose a novel training mechanism that simultaneously enforces equivariance and invariance to a general set of geometric transformations.
  arXiv  Detail & Related papers  (2021-03-01T21:14:33Z)
• Importance sampling of randomized measurements for probing entanglement [0.0]
  We show that combining randomized measurement protocols with importance sampling allows for characterizing entanglement in significantly larger quantum systems. A drastic reduction of statistical errors is obtained using machine-learning and tensor networks using partial information on the quantum state.
  arXiv  Detail & Related papers  (2021-02-26T14:55:53Z)
• Characterizing the loss landscape of variational quantum circuits [77.34726150561087]
  We introduce a way to compute the Hessian of the loss function of VQCs. We show how this information can be interpreted and compared to classical neural networks.
  arXiv  Detail & Related papers  (2020-08-06T17:48:12Z)
• Measurement-induced quantum criticality under continuous monitoring [0.0]
  We investigate entanglement phase transitions from volume-law to area-law entanglement in a quantum many-body state under continuous position measurement. We find the signatures of the transitions as peak structures in the mutual information as a function of measurement strength. We propose a possible experimental setup to test the predicted entanglement transition based on the subsystem particle-number fluctuations.
  arXiv  Detail & Related papers  (2020-04-24T19:35:28Z)
• Entanglement distance for arbitrary $M$-qudit hybrid systems [0.0]
  We propose a measure of entanglement which can be computed for pure and mixed states of a $M$-qudit hybrid system. We quantify the robustness of entanglement of a state through the eigenvalues analysis of the metric tensor associated with it.
  arXiv  Detail & Related papers  (2020-03-11T15:16:36Z)
• Remote Quantum Sensing with Heisenberg Limited Sensitivity in Many Body Systems [0.0]
  We propose a new way of doing quantum sensing. It exploits the dynamics of a many-body system, in a product state, along with a sequence of projective measurements in a specific basis.
  arXiv  Detail & Related papers  (2020-03-04T19:55:57Z)
• Generalized Sliced Distances for Probability Distributions [47.543990188697734]
  We introduce a broad family of probability metrics, coined as Generalized Sliced Probability Metrics (GSPMs) GSPMs are rooted in the generalized Radon transform and come with a unique geometric interpretation. We consider GSPM-based gradient flows for generative modeling applications and show that under mild assumptions, the gradient flow converges to the global optimum.
  arXiv  Detail & Related papers  (2020-02-28T04:18:00Z)

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.