Saturable global quantum sensing with Gaussian probes

• URL: http://arxiv.org/abs/2410.12050v1
• Date: Tue, 15 Oct 2024 20:37:38 GMT
• Title: Saturable global quantum sensing with Gaussian probes
• Authors: Chiranjib Mukhopadhyay, Matteo G. A. Paris, Abolfazl Bayat,
• Abstract summary: We provide an operationally motivated approach to global sensing in the frequentist picture. Our scheme yields a saturable bound on average uncertainty and allows for optimizing the measurement as well as the probe preparation simultaneously.
• Score: 0.0
• License:
• Abstract: Conventional formulation of quantum sensing has been mostly developed in the context of local estimation, where the unknown parameter is roughly known. In contrast, global sensing, where the prior information is incomplete and the unknown parameter is only known to lie within a broad interval, is practically more engaging but has received far less theoretical attention. Available formulations of global sensing rely on adaptive Bayesian strategies or minimizing average uncertainty. These methods either rely on challenging adaptive measurements or provide unsaturable bounds. Here, we provide an operationally motivated approach to global sensing in the frequentist picture. Our scheme yields a saturable bound on average uncertainty and allows for optimizing the measurement as well as the probe preparation simultaneously. We illustrate the implications for Gaussian single-mode sensing tasks like thermometry and phase estimation by showing that the optimal measurement indeed changes from homodyne, for local sensing, towards heterodyne, for global sensing. Depending on the task, this transformation can be gradual or sudden.

Related papers

• Current Trends in Global Quantum Metrology [0.0]
  Quantum sensors are universally acknowledged as one of the most promising near-term quantum technologies. We review some of the emerging developments in global quantum estimation. In the first approach, in order to achieve the best performance, one has to optimize the measurement settings adaptively.
  arXiv  Detail & Related papers  (2024-11-06T11:39:58Z)
• Optimal estimation of pure states with displaced-null measurements [0.0]
  We revisit the problem of estimating an unknown parameter of a pure quantum state. We investigate null-measurement' strategies in which the experimenter aims to measure in a basis that contains a vector close to the true system state.
  arXiv  Detail & Related papers  (2023-10-10T16:46:24Z)
• Bayesian Renormalization [68.8204255655161]
  We present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference. The main insight of Bayesian Renormalization is that the Fisher metric defines a correlation length that plays the role of an emergent RG scale. We provide insight into how the Bayesian Renormalization scheme relates to existing methods for data compression and data generation.
  arXiv  Detail & Related papers  (2023-05-17T18:00:28Z)
• Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation [53.19926259132379]
  We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. An attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals.
  arXiv  Detail & Related papers  (2023-05-10T03:40:25Z)
• Scalable Bayesian Meta-Learning through Generalized Implicit Gradients [64.21628447579772]
  Implicit Bayesian meta-learning (iBaML) method broadens the scope of learnable priors, but also quantifies the associated uncertainty. Analytical error bounds are established to demonstrate the precision and efficiency of the generalized implicit gradient over the explicit one.
  arXiv  Detail & Related papers  (2023-03-31T02:10:30Z)
• Instance-Dependent Generalization Bounds via Optimal Transport [51.71650746285469]
  Existing generalization bounds fail to explain crucial factors that drive the generalization of modern neural networks. We derive instance-dependent generalization bounds that depend on the local Lipschitz regularity of the learned prediction function in the data space. We empirically analyze our generalization bounds for neural networks, showing that the bound values are meaningful and capture the effect of popular regularization methods during training.
  arXiv  Detail & Related papers  (2022-11-02T16:39:42Z)
• Fundamental limits in Bayesian thermometry and attainability via adaptive strategies [0.0]
  We investigate the limits of thermometry using quantum probes at thermal equilibrium within the Bayesian approach. We obtain an ultimate bound on thermometry precision in the Bayesian setting, valid for arbitrary interactions and measurement schemes. We derive a no-go theorem for non-adaptive protocols that does not allow for better than linear (shot-noise-like) scaling.
  arXiv  Detail & Related papers  (2021-08-12T19:14:20Z)
• Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization [89.7882166459412]
  gradient noise (SGN) acts as implicit regularization for deep learning. Some works attempted to artificially simulate SGN by injecting random noise to improve deep learning. For simulating SGN at low computational costs and without changing the learning rate or batch size, we propose the Positive-Negative Momentum (PNM) approach.
  arXiv  Detail & Related papers  (2021-03-31T16:08:06Z)
• Global sensing and its impact for quantum many-body probes with criticality [0.0]
  Most quantum sensing protocols operate efficiently only when the unknown parameters vary within a very narrow region. In many-body probes, in which extra tunable parameters exist, our protocol can tune the performance for harnessing the quantum criticality. We show that even a simple magnetization measurement significantly benefits from our optimization and moderately delivers the theoretical precision.
  arXiv  Detail & Related papers  (2021-02-07T16:40:17Z)
• Temporal Difference Uncertainties as a Signal for Exploration [76.6341354269013]
  An effective approach to exploration in reinforcement learning is to rely on an agent's uncertainty over the optimal policy. In this paper, we highlight that value estimates are easily biased and temporally inconsistent. We propose a novel method for estimating uncertainty over the value function that relies on inducing a distribution over temporal difference errors.
  arXiv  Detail & Related papers  (2020-10-05T18:11:22Z)
• Bayesian parameter estimation using Gaussian states and measurements [0.0]
  We consider three paradigmatic estimation schemes in continuous-variable quantum metrology. We investigate the precision achievable with single-mode Gaussian states under homodyne and heterodyne detection. This allows us to identify Bayesian estimation strategies that combine good performance with the potential for straightforward experimental realization.
  arXiv  Detail & Related papers  (2020-09-08T12:54:12Z)

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.