Frequency estimation under non-Markovian spatially correlated quantum
noise: Restoring superclassical precision scaling
- URL: http://arxiv.org/abs/2204.10798v2
- Date: Tue, 1 Nov 2022 15:40:14 GMT
- Title: Frequency estimation under non-Markovian spatially correlated quantum
noise: Restoring superclassical precision scaling
- Authors: Francisco Riberi, Leigh M. Norris, Felix Beaudoin, and Lorenza Viola
- Abstract summary: We study the Ramsey estimation precision attainable by entanglement-enhanced interferometry in the presence of correlatedly non-classical noise.
In a paradigmatic case of spin-boson dephasovian noise from a thermal environment, we find that it is possible to suppress, on average, the effect of correlations by randomizing the location of probes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the estimation precision attainable by entanglement-enhanced Ramsey
interferometry in the presence of spatiotemporally correlated non-classical
noise. Our analysis relies on an exact expression of the reduced density matrix
of the qubit probes under general zero-mean Gaussian stationary dephasing,
which is established through cumulant-expansion techniques and may be of
independent interest in the context of non-Markovian open dynamics. By
continuing and expanding our previous work [Beaudoin et al., Phys. Rev. A 98,
020102(R) (2018)], we analyze the effects of a non-collective coupling regime
between the qubit probes and their environment, focusing on two limiting
scenarios where the couplings may take only two or a continuum of possible
values. In the paradigmatic case of spin-boson dephasing noise from a thermal
environment, we find that it is possible to suppress, on average, the effect of
spatial correlations by randomizing the location of the probes, as long as
enough configurations are sampled where noise correlations are negative. As a
result, superclassical precision scaling is asymptotically restored for initial
entangled states, including experimentally accessible one-axis spin-squeezed
states.
Related papers
- Mixed-State Topological Order under Coherent Noises [2.8391355909797644]
We find remarkable stability of mixed-state topological order under random rotation noise with axes near the $Y$-axis of qubits.
The upper bounds for the intrinsic error threshold are determined by these phase boundaries, beyond which quantum error correction becomes impossible.
arXiv Detail & Related papers (2024-11-05T19:00:06Z) - Acceleration Noise Induced Decoherence in Stern-Gerlach Interferometers for Gravity Experiments [0.0]
Acceleration noises can cause decoherence problems of Stern-Gerlach interferometer.
I will theoretically study these mechanisms of decoherence based on the analytical time-evolution operator of an SGI.
arXiv Detail & Related papers (2024-06-16T07:54:34Z) - Stochastic action for the entanglement of a noisy monitored two-qubit
system [55.2480439325792]
We study the effect of local unitary noise on the entanglement evolution of a two-qubit system subject to local monitoring and inter-qubit coupling.
We construct a Hamiltonian by incorporating the noise into the Chantasri-Dressel-Jordan path integral and use it to identify the optimal entanglement dynamics.
Numerical investigation of long-time steady-state entanglement reveals a non-monotonic relationship between concurrence and noise strength.
arXiv Detail & Related papers (2024-03-13T11:14:10Z) - Nearly Heisenberg-limited noise-unbiased frequency estimation by
tailored sensor design [0.0]
We consider entanglement-assisted frequency estimation by Ramsey interferometry.
We show that noise renders standard measurement statistics biased or ill-defined.
We introduce ratio estimators which, at infinite cost of doubling the resources, are insensitive to noise and retain the precision scaling of standard ones.
arXiv Detail & Related papers (2023-05-01T17:32:55Z) - Autonomous coherence protection of a two-level system in a fluctuating
environment [68.8204255655161]
We re-examine a scheme originally intended to remove the effects of static Doppler broadening from an ensemble of non-interacting two-level systems (qubits)
We demonstrate that this scheme is far more powerful and can also protect a single (or even an ensemble) qubit's energy levels from noise which depends on both time and space.
arXiv Detail & Related papers (2023-02-08T01:44:30Z) - Robust Inference of Manifold Density and Geometry by Doubly Stochastic
Scaling [8.271859911016719]
We develop tools for robust inference under high-dimensional noise.
We show that our approach is robust to variability in technical noise levels across cell types.
arXiv Detail & Related papers (2022-09-16T15:39:11Z) - Characterizing low-frequency qubit noise [55.41644538483948]
Fluctuations of the qubit frequencies are one of the major problems to overcome on the way to scalable quantum computers.
The statistics of the fluctuations can be characterized by measuring the correlators of the outcomes of periodically repeated Ramsey measurements.
This work suggests a method that allows describing qubit dynamics during repeated measurements in the presence of evolving noise.
arXiv Detail & Related papers (2022-07-04T22:48:43Z) - 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) - Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse
Separation with Spatial-Spectral Total Variation Regularization [49.55649406434796]
We propose a novel non particular approach to robust principal component analysis for HSI denoising.
We develop accurate approximations to both rank and sparse components.
Experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method.
arXiv Detail & Related papers (2022-01-08T11:48:46Z) - 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)
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.