Object detection and rangefinding with quantum states using simple
detection
- URL: http://arxiv.org/abs/2307.10785v3
- Date: Sun, 6 Aug 2023 21:51:44 GMT
- Title: Object detection and rangefinding with quantum states using simple
detection
- Authors: Richard J. Murchie, Jonathan D. Pritchard, John Jeffers
- Abstract summary: We present a theoretical framework for analysing coincident multi-shot data with simple detectors.
We quantify the advantage of quantum over classical illumination when performing target discrimination in a noisy thermal environment.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In a noisy environment with weak single levels, quantum illumination can
outperform classical illumination in determining the presence and range of a
target object even in the limit of sub-optimal measurements based on
non-simultaneous, phase-insensitive coincidence counts. Motivated by realistic
experimental protocols, we present a theoretical framework for analysing
coincident multi-shot data with simple detectors. This approach allows for the
often-overlooked non-coincidence data to be included, as well as providing a
calibration-free threshold for inferring the presence and range of an object,
enabling a fair comparison between different detection regimes. Our results
quantify the advantage of quantum over classical illumination when performing
target discrimination in a noisy thermal environment, including estimating the
number of shots required to detect a target with a given confidence level.
Related papers
- Entanglement-Enhanced Neyman-Pearson Target Detection [0.0]
Quantum illumination provides entanglement-enabled target-detection enhancement, despite operating in an entanglement-breaking environment.
Existing experimental studies of QI have utilized a Bayesian approach, assuming that the target is equally likely to be present or absent before detection.
We adopt the Neyman-alarm criterion in lieu of the error probability for equally likely target absence or presence as our figure of merit for QI.
arXiv Detail & Related papers (2024-10-10T02:33:58Z) - Quantum Illumination Advantage for Classification Among an Arbitrary Library of Targets [1.0599607477285324]
Quantum illumination (QI) is the task of querying a scene using a transmitter probe whose quantum state is entangled with a reference beam retained in ideal storage.
We show that in the limit of low transmitter brightness, high loss, and high thermal background, there is a factor of four improvement in the Chernoff exponent of the error probability in discriminating any number of apriori-known reflective targets.
arXiv Detail & Related papers (2024-08-24T06:20:23Z) - Quantum Target Ranging for LiDAR [0.0]
We investigate Quantum Target Ranging in the context of multi-hypothesis testing and its applicability to real-world LiDAR systems.
We demonstrate that ranging is generally an easier task compared to the well-studied problem of target detection.
We then analyze the theoretical bounds and advantages of quantum ranging in the context of phase-insensitive measurements.
arXiv Detail & Related papers (2024-08-05T17:00:14Z) - Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic
Uncertainty [58.144520501201995]
Bi-Lipschitz regularization of neural network layers preserve relative distances between data instances in the feature spaces of each layer.
With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices.
We also propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution.
arXiv Detail & Related papers (2021-10-12T22:04:19Z) - A Low Rank Promoting Prior for Unsupervised Contrastive Learning [108.91406719395417]
We construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning.
Our hypothesis explicitly requires that all the samples belonging to the same instance class lie on the same subspace with small dimension.
Empirical evidences show that the proposed algorithm clearly surpasses the state-of-the-art approaches on multiple benchmarks.
arXiv Detail & Related papers (2021-08-05T15:58:25Z) - A local hidden-variable model for experimental tests of the GHZ puzzle [0.0]
This paper describes a physically motivated local hidden-variable model based on amplitude-threshold detection.
A curious emergent feature of the model is that detection efficiencies may depend upon which observables are chosen for measurement.
arXiv Detail & Related papers (2021-04-27T20:48:16Z) - Conditional preparation of non-Gaussian quantum optical states by
mesoscopic measurement [62.997667081978825]
Non-Gaussian states of an optical field are important as a proposed resource in quantum information applications.
We propose a novel approach involving displacement of the ancilla field into the regime where mesoscopic detectors can be used.
We conclude that states with strong Wigner negativity can be prepared at high rates by this technique under experimentally attainable conditions.
arXiv Detail & Related papers (2021-03-29T16:59:18Z) - Crosstalk Suppression for Fault-tolerant Quantum Error Correction with
Trapped Ions [62.997667081978825]
We present a study of crosstalk errors in a quantum-computing architecture based on a single string of ions confined by a radio-frequency trap, and manipulated by individually-addressed laser beams.
This type of errors affects spectator qubits that, ideally, should remain unaltered during the application of single- and two-qubit quantum gates addressed at a different set of active qubits.
We microscopically model crosstalk errors from first principles and present a detailed study showing the importance of using a coherent vs incoherent error modelling and, moreover, discuss strategies to actively suppress this crosstalk at the gate level.
arXiv Detail & Related papers (2020-12-21T14:20:40Z) - 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) - Entanglement Enhanced Estimation of a Parameter Embedded in Multiple
Phases [1.0828616610785522]
Quantum-enhanced sensing promises to improve the performance of sensing tasks using non-classical probes and measurements.
We propose a distributed distributed sensing framework that uses an entangled quantum probe to estimate a scene- parameter encoded within an array of phases.
We apply our framework to examples as diverse as radio-frequency phased-array directional radar, beam-displacement tracking for atomic-force microscopy, and fiber-based temperature gradiometry.
arXiv Detail & Related papers (2020-04-08T17:59:33Z) - Adaptive Object Detection with Dual Multi-Label Prediction [78.69064917947624]
We propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection.
The model exploits multi-label prediction to reveal the object category information in each image.
We introduce a prediction consistency regularization mechanism to assist object detection.
arXiv Detail & Related papers (2020-03-29T04:23:22Z)
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.