Quantum Multi-User Detection Based on Coherent State Signals

Multi-user detection is one of the important technical problems for moderncommunications. In the field of quantum communication, the multi-access channel onwhich we apply the technology of quantum information processing is still an openquestion. In this work, we investigate the multi-user detection problem based on thebinary coherent-state signals whose communication way is supposed to be seen as aquantum channel. A binary phase shift keying model of this multi-access channel isstudied and a novel method of quantum detection proposed according to the conclusionof the quantum measurement theory. As a result, the average interference betweendeferent users is presented and the average error probability of the quantum detection isderived theoretically. Finally, we show the maximum channel capacity of this effectivedetection for a two-access quantum channel.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.

Quantum stochastic filters for nonlinear time-domain filtering of communication signals

Principles and performances of quantum stochastic filters are studied for nonlinear time-domain filtering of communication signals. Filtering is realized by combining neural networks with the nonlinear Schroedinger equation and the time-variant probability density function of signals is estimated by solution of the equation. It is shown that obviously different performances can be achieved by the control of weight coefficients of potential fields. Based on this characteristic, a novel filtering algorithm is proposed, and utilizing this algorithm, the nonlinear waveform distortion of output signals and the denoising capability of the filters can be compromised. This will make the application of quantum stochastic filters be greatly extended, such as in applying the filters to the processing of communication signals. The predominant performance of quantum stochastic filters is shown by simulation results.

Quantum-Classical Algorithm for an Instantaneous Spectral Analysis of Signals:A Complement to Fourier Theory

A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it’s based on Schrödinger’s equation. In the classical world, it is named frequency in time (FIT), which is used here as a complement of the traditional frequency-dependent spectral analysis based on Fourier theory. Besides, FIT is a metric which assesses the impact of the flanks of a signal on its frequency spectrum, not taken into account by Fourier theory and lets alone in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages, among others: 1) compact support with excellent energy output treatment, 2) low computational cost, O(N) for signals and O(N2) for images, 3) it does not have phase uncertainties (i.e., indeterminate phase for a magnitude = 0) as in the case of Discrete and Fast Fourier Transform (DFT, FFT, respectively). Finally, we can apply QSA to a quantum signal, that is, to a qubit stream in order to analyze it spectrally.

Stochastic resonance in a gain-noise model of a single-mode laser driven by pump noise and quantum noise with cross-correlation between real and imaginary parts under direct signal modulation

Stochastic resonance （SR） is studied in a gain-noise model of a single-mode laser driven by a coloured pump noise and a quantum noise with cross-correlation between real and imaginary parts under a direct signal modulation. By using a linear approximation method, we find that the SR appears during the variation of signal-to-noise ratio （SNR） separately with the pump noise self-correlation time τ, the noise correlation coefficient between the real part and the imaginary part of the quantum noise λq, the attenuation coefficient γ＇ and the deterministic steady-state intensity I0. In addition, it is found that the SR can be characterized not only by the dependence of SNR on the noise variables of and λq, but also by the dependence of SNR on the laser system variables of γ and I0. Thus our investigation extends the characteristic quantity of SR proposed before.

Large Signal Circuit Model of Two-Section Gain Lever Quantum Dot Laser

An equivalent circuit model for the design and analysis of two-section gain lever quantum dot(QD)laser is presented.This model is based on the three level rate equations with two independent carrier populations and a single longitudinal optical mode.By using the presented model,the effect of gain lever on QD laser performances is investigated.The results of simulation show that the main characteristics of laser such as threshold current,transient response,output power and modulation response are affected by differential gain ratios between the two-sections.

Large Signal Modulation Characteristics in the Transition Regime for Two-State Lasing Quantum Dot Lasers

Large-signal modulation capability, as an important performance indicator, is directly related to the high-speed optical communication technology involved. We experimentally and theoretically investigate the large-signal modulation characteristics of the simultaneous ground-state （GS） and the excited-state （ES） lasing in InAs/OaAs quantum dot laser diodes. The large-signal modulation capability of total light intensity in the transition regime from OS lasing to two-state lasing is unchanged as the bias-current increases. However, GS and ES large-signal eye diagrams show obvious variations during the transition. Relaxation oscillations and large-signal eye diagrams for OS, ES, and total light intensities are numerically simulated and analyzed in detail by using a rate-equation model. The -ndings show that a complementary relationship between the light intensities for OS and ES lasing exists in both the transition regime and the two-state lasing regime, leading to a much smaller overshooting power and a shorter settling time for the total light intensity. Therefore, the eye diagrams of GS or ES lasing are diffuse whereas those of total light intensity are constant as the bias-current increases in the transition regime.

Quantum Computational Techniques for Prediction of Cognitive State of Human Mind from EEG Signals

The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems.The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body.Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind.One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables,which provide more data density due to its exponential state-space growth.These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities.This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind,as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state.This paper implements and compares some techniques like Variational Quantum Classifiers(VQC),quantum annealing classifiers,and hybrid quantum-classical neural networks,to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data.It also introduces a novel pipeline by logically combining some of the aforementioned techniques,to predict future cognitive responses.The preliminary results of these approaches are presented and are very encouraging with upto 61.53%validation accuracy.

Observation of intermolecular double-quantum coherence signal dips in nuclear magnetic resonance

The correlated spectroscopy revamped by asymmetric Z-gradient echo detection （CRAZED） sequence is modified to investigate intermolecular double-quantum coherence nuclear magnetic resonance signal dips in highly polarized spin systems. It is found that the occurrence of intermolecular double-quantum coherence signal dips is related to sample geometry, field inhomogeneity and dipolar correlation distance. If the field inhomogeneity is refocused, the signal dip occurs at a fixed position whenever the dipolar correlation distance approaches the sample dimension. However, the position is shifted when the field inhomogeneity exists. Experiments and simulations are performed to validate our theoretic analysis. These signal features may offer a unique way to investigate porous structures and may find applications in biomedicine and material science.

Digital Multi-Signature Based on the Controlled Quantum Teleportation

In this paper, a scheme which can be used in multi-user quantum digital signature is proposed. The scheme of signature and verification is based on the characters of GHZ （Greenberger-Horne-Zeilinger） states and controlled quantum teleportation. Different from the digital signatures based on computational complexity, this scheme is unconditional secure, and compared to the former presented quantum signature scheme, it does not rely on an arbitrator to verify the signature and realize a message can be signed by multi-user together.

Quantum dots enhanced Cerenkov luminescence imaging

Cerenkov luminescence imaging(CLI) has been widely investigated for biological imaging. However, the luminescence generated from Cerenkov effect is relatively weak and has poor penetration ability in biological tissues.These limitations consequently hindered the clinical translation of CLI. In this study, we proposed an in vitro experimental study for the demonstration of quantum dots(QDs) configurations affected by the improvement of the signal intensity of CLI. Results revealed that the optimal concentrations were 0.1 mg/mL and 0.25 mg/mL for the studied CdSe/ZnS QDs with fluorescence emission peaks of 580 nm and 660 nm, respectively. The detected optical signal intensity with long-wavelength emission QDs were stronger than those with short-wavelength emission QDs.This study illustrates an experiment to study the effects of concentrations and fluorescence emission peaks of QDs on an enhanced optical signal for the external detection of CLI.

Super-Quantum Correlations: A Necessary Clarification

This paper proposes a necessary clarification about the problematic of super-quantum correlations, whose mainstream debate relies on an incorrect, statistical interpretation of the no-signaling condition. The no-signaling condition is an informational constraint that limits the strength of non-local correlations to the Tsirelson bound.

Emerging trends of integrated-mixed-signal chips in ISSCC 2023

In February 2023,the annual‘Olympic’of the chip industry,ISSCC,returned to in-person meetings in San Francisco.As shown in Fig.1,from the ISSCC 2023,we can see that the trend in IC design has shifted from pure digital to mixed-signal design,particularly in areas such as AI accelerator chips and quantum computing.Chips have also evolved from integrated circuits to integrated chips through modulelevel,function-level,and chip-level fusion,with compute-inmemory being the best embodiment of module-level and function-level fusion.Moreover,quantum computing was one of the major focuses of this ISSCC edition,with a separate paper session dedicated to cryo-CMOS for quantum computing.

A Sufficient Clarification of “Super-Quantum Correlations: A Necessary Clarification” by Pierre Uzan

In the paper “Super-Quantum Correlations: A Necessary Clarification” by Uzan [1], it is suggested that stronger than quantum (or supra-quantum) correlations are not possible. The main point of Uzan’s argumentation is the belief that the intuitive definition of No-Signalling (NS) is different from the statistical definition of No-Signalling (NSstat), and that situations exist where NSstat is respected while NS isn’t. In this paper we show why these definitions are one and the same, and where the example from the original paper breaks down. We provide a broader context to help the reader understand intuitively the situation.

超导心磁信号测量技术及冠心病筛查

目的:为解决微弱心磁信号测量难题和验证临床应用效果,研究超导心磁信号测量技术及冠心病筛查。方法:首先,分析超导心磁信号测量技术原理,并构建超导心磁信号测量技术框架。其次,设计主机控制器、磁通锁定环、信号调理模块等关键电路,并研制软件系统和核心算法。最后,入组心脏健康、轻度心肌缺血和重度心肌缺血共计255例受检志愿者,分别同步心磁检查和冠脉造影术(CAG)检查进行对比试验。结果:对比试验结果表明:(1)健康受检者电流密度向量分布存在对等双极结构,心肌缺血受检者电流密度向量分布不存在对等双极结构;(2)心磁筛查与CAG筛查总体符合率Cr、总体Kappa值、重度缺血符合率、轻度缺血符合率、阴性符合率分别为86.22%、78.21%、86.79%、84.09%、86.67%;(3)心磁筛查结果真实性评价指标即敏感度、特异度、阳性率、阴性率、阳性似然比、阴性似然比分别为87.16%、84.41%、86.00%、86.67%、5.98、0.08。结论:心磁筛查和CAG筛查在心肌缺血诊断方面具有较高的符合率和一致性,超导心磁信号测量技术是一种准确又可靠的冠心病筛查方法。

超导量子干涉仪实验数据测量系统

针对目前SQUID超导量子实验对低噪声以及微弱信号测量精度、灵敏度、采集速度等的需求,本文基于单片机STM32H750核心板和AD8429放大器,制作了一款SQUID实验数据测量系统.由单片机实测曲线图,经过处理分析得出超导量子信号RMS噪声值为2.009μV,与理论极限噪声值比较接近.系统还测得了环形液滴约瑟夫森结干涉效应曲线,其周期整齐均等,且峰-峰值基本一致.实验表明该系统具有一定的稳定性和准确性,可为研发大学物理SQUID演示仪奠定基础.

基于海上观测值的舰船导航信号滤波方法研究

为弥补单一数据源不足,并在不同航行环境下,确保导航信号的稳定性与可靠性,研究基于海上观测值的舰船导航信号滤波方法。在舰船导航中,基于纬度、经度、海流分量、航速、航向和角速度变化量等观测值建立了导航信号模型。利用改进量子粒子群优化算法,优化卡尔曼滤波算法的观测噪声协方差矩阵,以及舰船导航信号模型噪声协方差矩阵,得到改进卡尔曼滤波算法,结合舰船导航信号模型,获取舰船导航信号滤波估计结果。实验结果证明,该方法可有效滤波估计舰船导航信号,提升舰船导航信号质量;该方法改进后的舰船导航轨迹与期望轨迹非常接近,即改进后该方法的舰船导航信号滤波效果较优。

面向异构信号处理平台的量子调度算法

针对异构信号处理平台中已有调度算法的调度长度较大导致信号处理应用实时性下降问题,文中提出一种面向异构信号处理平台的量子调度算法。该算法采用任务优先级分流排序策略,得到更加准确的任务调度顺序。使用量子比特对任务分配方案进行编码,增加任务分配方案的多样性,且编码规则有助于跳出局部最优找到全局最优解。按照最小计算开销原则和任务复制思想进行处理器分配,减少任务间通信开销,并通过量子旋转门对量子编码方案进行更新,不断逼近最优解。仿真结果表明,所提算法能够减少调度长度,提升信号处理应用的实时性,进而提高平台的工作效率。