Optimally Displaced Threshold Detection for Discriminating Binary Coherent States Using Imperfect Devices

• URL: http://arxiv.org/abs/2007.11109v1
• Date: Tue, 21 Jul 2020 21:52:29 GMT
• Title: Optimally Displaced Threshold Detection for Discriminating Binary Coherent States Using Imperfect Devices
• Authors: Renzhi Yuan, Mufei Zhao, Shuai Han, and Julian Cheng
• Abstract summary: We analytically study the performance of the generalized Kennedy receiver having optimally displaced threshold detection (ODTD) in a realistic situation with noises and imperfect devices. We show that the proposed greedy search algorithm can obtain a lower and smoother error probability than the existing works.
• Score: 50.09039506170243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Because of the potential applications in quantum information processing tasks, discrimination of binary coherent states using generalized Kennedy receiver with maximum a posteriori probability (MAP) detection has attracted increasing attentions in recent years. In this paper, we analytically study the performance of the generalized Kennedy receiver having optimally displaced threshold detection (ODTD) in a realistic situation with noises and imperfect devices. We first prove that the MAP detection for a generalized Kennedy receiver is equivalent to a threshold detection in this realistic situation. Then we analyze the properties of the optimum threshold and the optimum displacement for ODTD, and propose a heuristic greedy search algorithm to obtain them. We prove that the ODTD degenerates to the Kennedy receiver with threshold detection when the signal power is large, and we also clarify the connection between the generalized Kennedy receiver with threshold detection and the one-port homodyne detection. Numerical results show that the proposed heuristic greedy search algorithm can obtain a lower and smoother error probability than the existing works.

Related papers

• Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection [63.93728560200819]
  Unsupervised out-of-distribution (U-OOD) detection is to identify data samples with a detector trained solely on unlabeled in-distribution (ID) data. Recent studies have developed various detectors based on DGMs to move beyond likelihood. We apply two techniques for each direction, specifically post-hoc prior and dataset entropy-mutual calibration. Experimental results demonstrate that the Resultant could be a new state-of-the-art U-OOD detector.
  arXiv  Detail & Related papers  (2024-09-05T02:58:13Z)
• Bagged Regularized $k$-Distances for Anomaly Detection [9.899763598214122]
  We propose a new distance-based algorithm called bagged regularized $k$-distances for anomaly detection (BRDAD) Our BRDAD algorithm selects the weights by minimizing the surrogate risk, i.e., the finite sample bound of the empirical risk of the bagged weighted $k$-distances for density estimation (BWDDE) On the theoretical side, we establish fast convergence rates of the AUC regret of our algorithm and demonstrate that the bagging technique significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2023-12-02T07:00:46Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory [14.725386295605666]
  We introduce a two-block RNN model that approximates the inference steps of the Bayesian filtering framework. We show that the proposed model improves the prediction accuracy compared to the three baseline imputation methods. We also show that our proposed method can achieve better prediction compared to the baselines when there is no miss-detection.
  arXiv  Detail & Related papers  (2022-03-14T13:39:44Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• Quantum-soft QUBO Suppression for Accurate Object Detection [8.871042314510788]
  Non-maximum suppression (NMS) has been adopted by default for removing redundant object detections for decades. We propose Quantum-soft QUBO Suppression (QSQS) algorithm for fast and accurate detection by exploiting quantum computing advantages.
  arXiv  Detail & Related papers  (2020-07-28T05:12:51Z)
• BiDet: An Efficient Binarized Object Detector [96.19708396510894]
  We propose a binarized neural network learning method called BiDet for efficient object detection. Our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal. Our method outperforms the state-of-the-art binary neural networks by a sizable margin.
  arXiv  Detail & Related papers  (2020-03-09T08:16:16Z)
• RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies [11.02452262854759]
  Outlier detection is an important task in data mining and many technologies have been explored in various applications. We propose a novel detection model Dual-GAN, which can directly utilize the potential information in identified anomalies to detect discrete outliers simultaneously. In addition, to deal with the evaluation of Nash equilibrium and the selection of optimal model, two evaluation indicators are created and introduced into the two models to make the detection process more intelligent.
  arXiv  Detail & Related papers  (2020-03-07T17:13:52Z)

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.