Reliable Interval Estimation for the Fidelity of Entangled States in Scenarios with General Noise
- URL: http://arxiv.org/abs/2409.19282v1
- Date: Sat, 28 Sep 2024 08:23:23 GMT
- Title: Reliable Interval Estimation for the Fidelity of Entangled States in Scenarios with General Noise
- Authors: Liangzhong Ruan, Bas Dirkse,
- Abstract summary: fidelity estimation for entangled states is an essential building block for quality control and error detection in quantum networks.
Quantum networks often encounter heterogeneous and correlated noise, leading to excessive uncertainty in the estimated fidelity.
This paper proposes a credible interval for fidelity that is valid in the presence of general noise.
- Score: 0.27624021966289597
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fidelity estimation for entangled states constitutes an essential building block for quality control and error detection in quantum networks. Nonetheless, quantum networks often encounter heterogeneous and correlated noise, leading to excessive uncertainty in the estimated fidelity. In this paper, the uncertainty associated with the estimated fidelity under conditions of general noise is constrained by jointly employing random sampling, a thought experiment, and Bayesian inference, resulting in a credible interval for fidelity that is valid in the presence of general noise. The proposed credible interval incorporates all even moments of the posterior distribution to enhance estimation accuracy. Factors influencing the estimation accuracy are identified and analyzed. Specifically, the issue of excessive measurements is addressed, emphasizing the necessity of properly determining the measurement ratio for fidelity estimation under general noise conditions.
Related papers
- Error minimization for fidelity estimation of GHZ states with arbitrary noise [0.32634122554913997]
This work studies a scenario in which multiple nodes share noisy Greenberger-Horne-Zeilinger (GHZ) states.
Due to the collapsing nature of quantum measurements, the nodes randomly sample a subset of noisy GHZ states for measurement.
The proposed protocol achieves the minimum mean squared estimation error in a challenging scenario characterized by arbitrary noise.
arXiv Detail & Related papers (2024-08-18T09:02:17Z) - Bayesian meta learning for trustworthy uncertainty quantification [3.683202928838613]
We propose, Trust-Bayes, a novel optimization framework for Bayesian meta learning.
We characterize the lower bounds of the probabilities of the ground truth being captured by the specified intervals.
We analyze the sample complexity with respect to the feasible probability for trustworthy uncertainty quantification.
arXiv Detail & Related papers (2024-07-27T15:56:12Z) - Score Matching-based Pseudolikelihood Estimation of Neural Marked
Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification.
Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching.
The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
arXiv Detail & Related papers (2023-10-25T02:37:51Z) - Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing
Uncertainty [125.41260574344933]
We consider an encoder-decoder based deep ensemble network for capturing both perception and predictive uncertainty simultaneously.
Overall, deep ensembles provided more robust predictions and the consideration of upstream uncertainty further increased the estimation accuracy for the model.
arXiv Detail & Related papers (2023-05-26T04:27:48Z) - 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) - Optimizing the Noise in Self-Supervised Learning: from Importance
Sampling to Noise-Contrastive Estimation [80.07065346699005]
It is widely assumed that the optimal noise distribution should be made equal to the data distribution, as in Generative Adversarial Networks (GANs)
We turn to Noise-Contrastive Estimation which grounds this self-supervised task as an estimation problem of an energy-based model of the data.
We soberly conclude that the optimal noise may be hard to sample from, and the gain in efficiency can be modest compared to choosing the noise distribution equal to the data's.
arXiv Detail & Related papers (2023-01-23T19:57:58Z) - Minimization of the estimation error for entanglement distribution
networks with arbitrary noise [1.3198689566654105]
We consider a setup in which nodes randomly sample a subset of the entangled qubit pairs to measure and then estimate the average fidelity of the unsampled pairs conditioned on the measurement outcome.
The proposed estimation protocol achieves the lowest mean squared estimation error in a difficult scenario with arbitrary noise and no prior information.
arXiv Detail & Related papers (2022-03-18T13:05:36Z) - Bayesian autoencoders with uncertainty quantification: Towards
trustworthy anomaly detection [78.24964622317634]
In this work, the formulation of Bayesian autoencoders (BAEs) is adopted to quantify the total anomaly uncertainty.
To evaluate the quality of uncertainty, we consider the task of classifying anomalies with the additional option of rejecting predictions of high uncertainty.
Our experiments demonstrate the effectiveness of the BAE and total anomaly uncertainty on a set of benchmark datasets and two real datasets for manufacturing.
arXiv Detail & Related papers (2022-02-25T12:20:04Z) - Dense Uncertainty Estimation via an Ensemble-based Conditional Latent
Variable Model [68.34559610536614]
We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model.
We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation.
Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
arXiv Detail & Related papers (2021-11-22T08:54:10Z) - Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
We describe two types of forecasting problems: regular grid-based and graph-based.
We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory.
Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
arXiv Detail & Related papers (2021-05-25T14:35:46Z)
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.