Entanglement Enhanced Sensing with Qubits affected by non-Markovian Dephasing

• URL: http://arxiv.org/abs/2604.22368v1
• Date: Fri, 24 Apr 2026 08:59:48 GMT
• Title: Entanglement Enhanced Sensing with Qubits affected by non-Markovian Dephasing
• Abstract summary: Entanglement has been proposed as a means to improve the sensitivity of sensing weak signals.<n>We show that entanglement can provide a substantial improvement for Ramsey spectroscopy subject to correlated noise.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entanglement has been proposed as a means to improve the sensitivity of sensing weak signals. While the degree of this quantum advantage is well understood in noiseless settings, the situation is more complex under realistic conditions, where the system is subject to decoherence. In this case, the enhancement depends on the specific noise characteristics. Previous treatments of colored noise typically assume that the noise is uncorrelated between successive experiments. Here, we consider the scenario in which the noise exhibits correlations across multiple shots. We derive a simple fundamental limit to the sensitivity based on the fact that the sensitivity cannot be better than the signal-to-noise ratio seen by the probe. Focusing on Ramsey spectroscopy with probes affected by pure classical dephasing, we show that, for suitable spatial and temporal noise correlations, entangled probes achieve a better scaling of the sensitivity with the number of probes than separable states. This demonstrates that entanglement can provide a substantial improvement for Ramsey spectroscopy subject to correlated noise.

Related papers

• Accurate noise spectroscopy using quantum sensing ensembles [70.36032466145112]
  We present an analysis procedure for accessing the true source noise spectrum using ensemble coherence.<n>We show that sequence timing plays a dual role in ensemble noise spectroscopy, setting both the frequency selectivity and the weighted collection of signal.<n>We apply this procedure to a model system, shallow nitrogen-vacancy centres in diamond, and show that consistent information about the spectral character of MHz-range surface noise can be obtained.
  arXiv  Detail & Related papers  (2026-09-15T05:03:36Z)
• Entanglement advantage in sensing power-law spatiotemporal noise correlations [3.6347018475682087]
  We compute the fundamental limits of quantum sensors in probing correlated signals (noise)<n>We derive a scalable entanglement advantage when power-law decays slowly.<n>Our protocols can be implemented using state-the-art sensing platforms including solid-state defects, superconducting circuits, and neutral atoms.
  arXiv  Detail & Related papers  (2026-03-16T18:00:03Z)
• Correlated Noise Estimation with Quantum Sensor Networks [18.51122677780099]
  We develop a theoretical framework to determine the limits of correlated (weak) noise estimation with quantum sensor networks.<n>We identify a sensing protocol, reminiscent of a many-body echo sequence, that achieves the fundamental limits of measurement sensitivity for a broad class of problems.
  arXiv  Detail & Related papers  (2024-12-23T19:00:06Z)
• Exponential entanglement advantage in sensing correlated noise [16.70008024600165]
  We propose a new form of exponential quantum advantage in the context of sensing correlated noise. We show that entanglement can lead to an exponential enhancement in the sensitivity for estimating a small parameter. Our work thus opens a novel pathway towards achieving entanglement-based sensing advantage.
  arXiv  Detail & Related papers  (2024-10-08T10:15:21Z)
• Quantifying Noise of Dynamic Vision Sensor [49.665407116447454]
  Dynamic visual sensors (DVS) are characterised by a large amount of background activity (BA) noise. It is difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. A new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA)
  arXiv  Detail & Related papers  (2024-04-02T13:43:08Z)
• Coherent interaction-free detection of noise [0.0]
  We propose interaction-free measurements as a noise-detection technique. We explore two conceptually different schemes: the coherent and the projective realizations. We study the signature of noise correlations in the detector's signal.
  arXiv  Detail & Related papers  (2023-12-28T18:24:13Z)
• Stabilizing and improving qubit coherence by engineering noise spectrum of two-level systems [52.77024349608834]
  Superconducting circuits are a leading platform for quantum computing. Charge fluctuators inside amorphous oxide layers contribute to both low-frequency $1/f$ charge noise and high-frequency dielectric loss. We propose to mitigate those harmful effects by engineering the relevant TLS noise spectral densities.
  arXiv  Detail & Related papers  (2022-06-21T18:37:38Z)
• High-Order Qubit Dephasing at Sweet Spots by Non-Gaussian Fluctuators: Symmetry Breaking and Floquet Protection [55.41644538483948]
  We study the qubit dephasing caused by the non-Gaussian fluctuators. We predict a symmetry-breaking effect that is unique to the non-Gaussian noise.
  arXiv  Detail & Related papers  (2022-06-06T18:02:38Z)
• Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders [62.997667081978825]
  We propose a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals.
  arXiv  Detail & Related papers  (2021-09-18T14:51:24Z)
• Shape Matters: Understanding the Implicit Bias of the Noise Covariance [76.54300276636982]
  Noise in gradient descent provides a crucial implicit regularization effect for training over parameterized models. We show that parameter-dependent noise -- induced by mini-batches or label perturbation -- is far more effective than Gaussian noise. Our analysis reveals that parameter-dependent noise introduces a bias towards local minima with smaller noise variance, whereas spherical Gaussian noise does not.
  arXiv  Detail & Related papers  (2020-06-15T18:31:02Z)
• Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles [65.9694455739978]
  We study contextual linear bandit problems under feature uncertainty, where the features are noisy and have missing entries. Our analysis reveals that the optimal hypothesis can significantly deviate from the underlying realizability function, depending on the noise characteristics. This implies that classical approaches cannot guarantee a non-trivial regret bound.
  arXiv  Detail & Related papers  (2017-03-03T21:39:56Z)

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.