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1.
As one of important research branches of quantum communication, deterministic remote state preparation (DRSP) plays a significant role in quantum network. Quantum noises are prevalent in quantum communication, and it can seriously affect the safety and reliability of quantum communication system. In this paper, we study the effect of quantum noise on deterministic remote state preparation of an arbitrary two-particle state via different quantum channels including the \(\chi \) state, Brown state and GHZ state. Firstly, the output states and fidelities of three DRSP algorithms via different quantum entangled channels in four noisy environments, including amplitude-damping, phase-damping, bit-flip and depolarizing noise, are presented, respectively. And then, the effects of noises on three kinds of preparation algorithms in the same noisy environment are discussed. In final, the theoretical analysis proves that the effect of noise in the process of quantum state preparation is only related to the noise type and the size of noise factor and independent of the different entangled quantum channels. Furthermore, another important conclusion is given that the effect of noise is also independent of how to distribute intermediate particles for implementing DRSP through quantum measurement during the concrete preparation process. These conclusions will be very helpful for improving the efficiency and safety of quantum communication in a noisy environment.  相似文献   

2.
Class Noise vs. Attribute Noise: A Quantitative Study   总被引:2,自引:0,他引:2  
Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on the data. Noise can reduce system performance in terms of classification accuracy, time in building a classifier and the size of the classifier. Accordingly, most existing learning algorithms have integrated various approaches to enhance their learning abilities from noisy environments, but the existence of noise can still introduce serious negative impacts. A more reasonable solution might be to employ some preprocessing mechanisms to handle noisy instances before a learner is formed. Unfortunately, rare research has been conducted to systematically explore the impact of noise, especially from the noise handling point of view. This has made various noise processing techniques less significant, specifically when dealing with noise that is introduced in attributes. In this paper, we present a systematic evaluation on the effect of noise in machine learning. Instead of taking any unified theory of noise to evaluate the noise impacts, we differentiate noise into two categories: class noise and attribute noise, and analyze their impacts on the system performance separately. Because class noise has been widely addressed in existing research efforts, we concentrate on attribute noise. We investigate the relationship between attribute noise and classification accuracy, the impact of noise at different attributes, and possible solutions in handling attribute noise. Our conclusions can be used to guide interested readers to enhance data quality by designing various noise handling mechanisms.  相似文献   

3.
于墨  赵铁军  胡鹏龙  郑德权 《软件学报》2013,24(10):2340-2353
噪声可学习性理论指出,有监督学习方法的性能会受到训练样本标记噪声的严重影响.然而,已有相关理论研究仅针对二类分类问题.致力于探究结构化学习问题受噪声影响的规律性.首先,注意到在结构化学习问题中,标注数据的噪声会在训练过程中被放大,使得训练过程中标记样本的噪声率高于标记样本的错误率.传统的噪声可学习性理论并未考虑结构化学习中的这一现象,从而低估了问题的复杂性.从结构化学习问题的噪声放大现象出发,提出了新的结构化学习问题的噪声可学习性理论.在此基础上,提出了有效训练数据规模的概念,这一指标可用于在实践中描述噪声学习问题的数据质量,并进一步分析了实际应用中的结构化学习模型在高噪声环境下向低阶模型回退的情况.实验结果证明了该理论的正确性及其在跨语言映射和协同训练方法中的应用价值和指导意义.  相似文献   

4.
含噪语音实时迭代维纳滤波   总被引:1,自引:1,他引:0       下载免费PDF全文
针对传统去噪方法在强背景噪声情况下,提取声音信号的能力变弱甚至失效与对不同噪声环境适应性差,提出了迭代维纳滤波声音信号特征提取方法。给出了语音噪声频谱与功率谱信噪比迭代更新机制与具体实施方案。实验仿真表明,该算法能有效地去噪滤波,显著地提高语音识别系统性能,且在不同的噪声环境和信噪比条件下具有鲁棒性。该算法计算代价小,简单易实现,适用于嵌入式语音识别系统。  相似文献   

5.
6.
根据不同尺度子带特征反映语音的不同细节特性,提出一种噪声下的多层子带(MLS)语音识别方法。将语音频谱分成多层多个子带,首先各子带分另单独进行识别,然后将各层各子带识别概率综合起来得到最终识别结果。将新方法应用于TIMIT数据饣E-Set在NoiseX92白噪声和F16噪声下识别实验。实验结果表明,多层子带方法在噪声环境和无噪情况下识别性能都有很大提高。  相似文献   

7.
Enhancing the fidelity of quantum state transmission in noisy environments is a significant subject in the field of quantum communication. In this paper, improving the fidelity of a deterministic remote state preparation (RSP) protocol under decoherence is investigated with the technique of weak measurement (WM) and weak measurement reversal (WMR). We first construct the quantum circuit of the deterministic remote preparation of a single-qubit state through an EPR state with the assistance of an auxiliary qubit. Then, we analytically derive the average fidelity of the deterministic RSP protocol under the influence of generalized amplitude damping noises acting on the EPR state. Our results show that when only qubit 2 undergoes the decoherence channel, the average fidelity of the RSP protocol subject to generalized amplitude damping noise is the same as that subject to amplitude damping noise. Moreover, we analyze the optimal average fidelity of the above RSP process by introducing WM and WMR. It is found that the application of WM and a subsequent reversal operation could lead to the remarkable improvement of the average fidelity for most values of the decoherence parameters.  相似文献   

8.

In this paper, we propose a hybrid speech enhancement system that exploits deep neural network (DNN) for speech reconstruction and Kalman filtering for further denoising, with the aim to improve performance under unseen noise conditions. Firstly, two separate DNNs are trained to learn the mapping from noisy acoustic features to the clean speech magnitudes and line spectrum frequencies (LSFs), respectively. Then the estimated clean magnitudes are combined with the phase of the noisy speech to reconstruct the estimated clean speech, while the LSFs are converted to linear prediction coefficients (LPCs) to implement Kalman filtering. Finally, the reconstructed speech is Kalman-filtered for further removing the residual noises. The proposed hybrid system takes advantage of both the DNN based reconstruction and traditional Kalman filtering, and can work reliably in either matched or unmatched acoustic environments. Computer based experiments are conducted to evaluate the proposed hybrid system with comparison to traditional iterative Kalman filtering and several state-of-the-art DNN based methods under both seen and unseen noises. It is shown that compared to the DNN based methods, the hybrid system achieves similar performance under seen noise, but notably better performance under unseen noise, in terms of both speech quality and intelligibility.

  相似文献   

9.
Multi-particle quantum state deterministic remote preparation is a fundamental and important technical branch in quantum communication. Since quantum noise is unavoidable in realistic quantum communication, it is important to analyze the effect of noise on multi-particle quantum communication protocols. In this paper, we study the effects of noise, such as amplitude damping, phase damping, bit-flip and depolarizing noises, on two deterministic remote preparation of an arbitrary three-particle state protocols, which are based on two different entangled channels, namely \(\chi \) state and Brown state. The detailed mathematical analysis shows that the output states of two deterministic remote state preparation (DRSP) protocols are the same in the same noisy environment. That is to say, in the same noisy environment, the effects of noise on two DRSP protocols are the same. This conclusion proves that these two DRSP protocols will produce the same arbitrary three-particle states in the same noise channel environment, and so that these protocols are inherently convergent and can be substituted for each other in certain circumstances. In addition, this paper also takes three-particle states \(a\left| {000} \right\rangle + b{\mathrm{e}^{ic}}\left| {111} \right\rangle \) as an example and studies the relationship between the fidelity, the target state and the size of the noise factor. The results show that if the target state can be selected, an appropriate target state can effectively resist on the bit-flip noise. If the target state cannot be selected, as the increase in the size of noise factor, the fidelities of the two DRSP schemes in the amplitude damping noise and phase damping noise are always larger than those in the bit-flip noise and depolarizing noise. This conclusion indicates that two protocols have better resistance on amplitude damping and phase damping noise than the bit-flip and depolarizing noises. These findings and analyses will provide valid help in deterministic remote preparation of an arbitrary three-particle state in a noisy environment.  相似文献   

10.
针对传统去噪方法在强背景噪声情况下,提取声音信号的能力变弱甚至失效与对不同噪声环境适应性差,提出了一种动态FRFT滤波声音信号语音增强方法。给出了不同语音噪声环境下FRFT最优聚散度的更新机制与具体实施方案。用TIMIT标准语音库与Noisex-92噪声库搭配,实验仿真表明,该算法能有效地去噪滤波,显著地提高语音识别系统性能,且在不同的噪声环境和信噪比条件下具有鲁棒性。算法计算代价小,简单易实现。  相似文献   

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