Controlled-NOT logic gate for phase qubits based on conditional spectroscopy

A controlled-NOT logic gate based on conditional spectroscopy has been demonstrated recently for a pair of superconducting flux qubits [Plantenberg et?al. in Nature 447:836, 2007]. Here we study the fidelity of this type of gate applied to a phase qubit coupled to a resonator (or a pair of capacitively coupled phase qubits). Our results show that an intrinsic fidelity of more than 99% is achievable in 45?ns.     

Checking sequences for distributed test architectures

Controllability and observability problems may manifest themselves during the application of a checking sequence in a test architecture where there are multiple remote testers. These problems often require the use of external coordination message exchanges among testers during testing. However, the use of coordination messages requires the existence of an external network that can increase the cost of testing and can be difficult to implement. In addition, the use of coordination messages introduces delays and this can cause problems where there are timing constraints. Thus, sometimes it is desired to construct a checking sequence from the specification of the system under test that will be free from controllability and observability problems without requiring the use of external coordination message exchanges. This paper gives conditions under which it is possible to produce such a checking sequence, using multiple distinguishing sequences, and an algorithm that achieves this.     

适应噪声环境的改进遗传算法

遗传算法领域考虑噪声存在的情况非常复杂,设计合理的改进方法提高遗传算法的性能非常必要.从改进算法设计策略的角度,基于引进平滑滤波的方法提出了改进方法,并利用计算机仿真实验,与现有方法比较,结果表明改进的遗传算法不仅提高了算法的收敛性能,并且提高了算法的计算速度.归纳并通过实验表明了现有方法的有效性.最后,从信噪比这样一个新的角度对噪声环境下的遗传算法进行分析,得出了有效的改进方案.     

Stable social foraging swarms in a noisy environment

Bacteria, bees, and birds often work together in groups to find food. A group of robots can be designed to coordinate their activities to search for and collect objects. Networked cooperative uninhabited autonomous vehicles are being developed for commercial and military applications. Suppose that we refer to all such groups of entities as "social foraging swarms". In order for such multiagent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli (e.g., by optimizing the acquisition of nutrients in foraging for food). In this paper, we characterize swarm cohesiveness as a stability property and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive foraging even in the presence of "noise" characterized by uncertainty on sensing other agent's position and velocity, and in sensing nutrients that each agent is foraging for. The results quantify earlier claims that social foraging is in a certain sense superior to individual foraging when noise is present, and provide clear connections between local agent-agent interactions and emergent group behavior. Moreover, the simulations show that very complicated but orderly group behaviors, reminiscent of those seen in biology, emerge in the presence of noise.     

Knowledge-based environment for investigating multicomputer architectures

Multicomputers for massively parallel processing will eventually employ billions of processing elements, each of which will be capable of communicating with every other processing element. A knowledge-based modelling and simulation environment (KBMSE) for investigating such multicomputer architecture at a discrete-event system level is described. The KBMSE implements the discrete-event system specification (DEVS) formalism in an object-oriented programming system of Scheme (a dialect), which supports building models in a hierarchical, modular manner, a systems-oriented approach not possible in conventional simulation languages. The paper presents a framework for knowledge-based modelling and simulation by exemplifying modelling a hypercube multicomputer architecture in the KBMSE. The KBMSE has been tested on a variety of domains characterized by complex, hierarchical structures such as advanced multicomputer architectures, local area computer networks, intelligent multi-robot organizations, and biologically based life-support systems.     

Auditory nerve representation as a front-end for speech recognition in a noisy environment

We describe here a computational model based upon the temporal characteristics of the information in the auditory nerve-fiber firing patterns. The model produces a frequency domain representation of the input signal in terms of the ensemble histogram of the inverse of the interspike intervals, measured from firing patterns generated by a simulated nerve-fiber array. The nerve-fiber mechanism is modeled by a multi-level-crossing detector at the output of each cochlear filter. We use 85 cochlear filters, equally spaced on a log-frequency scale from 200 Hz to 3200 Hz, and the level crossings are measured at positive threshold levels which are uniformly distributed in log scale. The resulting Ensemble Interval Histogram (EIH) pseudo spectrum shares two main properties: (1) fine spectral details are well preserved in the low-frequency region but become fuzzy at the high-frequency end; (2) the EIH spectrum is more robust in noise, compared with the traditional Fourier spectrum. This representation of the speech has been used as a front-end to a Dynamic Time Warping (DTW), speaker-dependent, isolated word recognizer. The database consisted of a 39-word alpha-digits vocabulary spoken by two male and two female speakers, in different levels of additive white noise. In the noise-free case, the performance of the EIH-based front-end is comparable to a conventional Fourier Transform (FFT)-based front-end. In the presence of noise, however, the EIH-based front-end is more robust. Compared with the FFT-based front-end, with increasing noise the recognition scores drop more slowly, the resulting gap increases as the SNR values decreases. Quantitatively, with the EIH-based front-end the recognizer achieves a given recognition score with global-SNR values which are between 5 dB and 15 dB lower.     

Statistical tolerancing using designed experiments in a noisy environment

We consider the method of designed experiments for statistical tolerance analysis, and study the impact of experimental error on its results. It is observed that presence of random error in the experiment environment (e.g. laboratory) could introduce bias in the moment estimators and increases their respective variances. We propose adjustments to the method that would reduce the bias as well as the variance of these estimators. A numerical example is presented.     

Asymmetric quantum dialogue in noisy environment

A notion of asymmetric quantum dialogue (AQD) is introduced. Conventional protocols of quantum dialogue are essentially symmetric as the users (Alice and Bob) can encode the same amount of classical information. In contrast, the proposed scheme for AQD provides different amount of communication powers to Alice and Bob. The proposed scheme offers an architecture, where the entangled state to be used and the encoding scheme to be shared between Alice and Bob depend on the amount of classical information they want to exchange with each other. The general structure for the AQD scheme has been obtained using a group theoretic structure of the operators introduced in Shukla et al. (Phys Lett A 377:518, 2013). The effect of different types of noises (e.g., amplitude damping and phase damping noise) on the proposed scheme is investigated, and it is shown that the proposed scheme for AQD is robust and it uses an optimized amount of quantum resources.     

On optimal distributed decision architectures in a hypothesistesting environment

The authors consider the distributed detection problem, in which a set of decision makers (DMs) receive observations of the environment and transmit finite-values messages to other DMs according to prespecified communication protocols. A designated primary DM makes the final decision on one out of two alternative hypothesis. All DMs make decisions, in order to maximize a measure of organizational performance. The problem is to find an architecture for the organization which remains optimal for a variety of operating conditions. It is shown that even for very small organizations this problem is quite complex because the optimal architecture depends on variables external to the team, so that global conclusions on optimal organizational structures cannot be drawn. Suboptimal solutions are considered, and bounds on their performance are obtained     

Scheme for implementing multitarget qubit controlled-NOT gate of photons and controlled-phase gate of electron spins via quantum dot-microcavity coupled system

We propose a deterministic scheme to implement the multiqubit controlled-NOT gate of photons and multiqubit controlled-phase gate of electron spins with one control qubit and multiple target qubits using quantum dots in double-sided optical cavities. The scheme is based on spin selective photon reflection from the cavity and can be achieved in a nondestructive way. We assess the feasibility of the scheme and show that the gates can be implemented with high average fidelities by choosing the realistic system parameters appropriately. The scheme is useful in quantum information processing such as entanglement preparation, quantum error correction, and quantum algorithms.     

Improved switching for multiple model adaptive controller in noisy environment

A new switching mechanism for multiple model adaptive controllers (MMAC) is suggested in this paper. The proposed method gives superior performance in comparison to the widely used method of switching based on performance function and hysteresis function in systems which experience high levels of measurement noise. This method acts as a complementary condition within the switching mechanism which checks the existence of excitation in system at every instant. The new method is evaluated by simulation studies on a nonlinear model of a pH neutralization plant.     

Topological design of a distributed computer communication network in a noisy environment

The effect of noisy links is considered in the design of a computer communication network topology. Because of noisy links, and the consequent probability of bit error, additional network traffic in the form of acknowledgement packets and retransmission packets are generated which load the network. A topological design procedure for obtaining a minimum cost network is proposed which satisfies given constraints of overall network delay, maximum delay on a link, throughput and reliability. Traffic priority for different classes of traffic is also taken into consideration in the design. As expected, the network cost increases as the bit error probability of the links increases.     

Identification of piecewise affine models in noisy environment

This paper addresses the identification of non-linear systems. A wide class of these systems can be described using non-linear time-invariant regression models, that can be approximated by means of piecewise affine prototypes with an arbitrary degree of accuracy. This work concerns the identification of piecewise affine model parameters through input-output data affected by additive noise. In order to show the effectiveness of the developed technique, the results obtained in the identification of both a simple simulated system and a real dynamic process are reported.     

Self-organizing neural architectures and cooperative learning in a multiagent environment.

Temporal-Difference-Fusion Architecture for Learning, Cognition, and Navigation (TD-FALCON) is a generalization of adaptive resonance theory (a class of self-organizing neural networks) that incorporates TD methods for real-time reinforcement learning. In this paper, we investigate how a team of TD-FALCON networks may cooperate to learn and function in a dynamic multiagent environment based on minefield navigation and a predator/prey pursuit tasks. Experiments on the navigation task demonstrate that TD-FALCON agent teams are able to adapt and function well in a multiagent environment without an explicit mechanism of collaboration. In comparison, traditional Q-learning agents using gradient-descent-based feedforward neural networks, trained with the standard backpropagation and the resilient-propagation (RPROP) algorithms, produce a significantly poorer level of performance. For the predator/prey pursuit task, we experiment with various cooperative strategies and find that a combination of a high-level compressed state representation and a hybrid reward function produces the best results. Using the same cooperative strategy, the TD-FALCON team also outperforms the RPROP-based reinforcement learners in terms of both task completion rate and learning efficiency.     

Hierarchical joint remote state preparation in noisy environment

A novel scheme for quantum communication having substantial applications in practical life is designed and analyzed. Specifically, we have proposed a hierarchical counterpart of the joint remote state preparation (JRSP) protocol, where two senders can jointly and remotely prepare a quantum state. One sender has the information regarding amplitude, while the other one has the phase information of a quantum state to be jointly prepared at the receiver’s port. However, there exists a hierarchy among the receivers, as far as powers to reconstruct the quantum state are concerned. A 5-qubit cluster state has been used here to perform the task. Further, it is established that the proposed scheme for hierarchical JRSP (HJRSP) is of enormous practical importance in critical situations involving defense and other sectors, where it is essential to ensure that an important decision/order that can severely affect a society or an organization is not taken by a single person, and once the order is issued, all the receivers do not possess an equal right to implement it. Further, the effect of different noise models (e.g., amplitude damping (AD), phase damping (PD), collective noise and Pauli noise models) on the HJRSP protocol proposed here is investigated. It is found that in AD and PD noise models a higher-power agent can reconstruct the quantum state to be remotely prepared with higher fidelity than that done by the lower-power agent(s). In contrast, the opposite may happen in the presence of collective noise models. We have also proposed a scheme for probabilistic HJRSP using a non-maximally entangled 5-qubit cluster state.     

Cyclic joint remote state preparation in noisy environment

We propose a scheme of cyclic joint remote state preparation for three sides, which takes advantage of three GHZ states to compose product state as quantum channel. Suppose there are six legitimate participants, says Alice, Bob, Charlie, David, Emma and Fred in the scheme. It can be shown that Alice and David can remotely prepare a single-qubit state on Bob’s side; meanwhile, Bob and Emma can remotely prepare a desired quantum state on Charlie’s side, and Charlie and Fred can also remotely prepare a single-qubit state on Alice’s side at the same time. Further, it can be achieved in the opposite direction of the cycle by changing the quantum channel. Based on it, we generalize this protocol to \(N (N\ge 3)\) sides utilizing three multi-qubit GHZ-type states as quantum channel. Therefore, the scheme can achieve cyclic joint remote state preparation, which remotely prepares N states in quantum network with N-party, simultaneously. In addition, we consider that the effect of amplitude-damping noise of the initial states is prepared in four different laboratory. Clearly, we use fidelity to describe how much information has been lost in the cyclic process. Our investigation about the effect of noise shows that the preparing of the initial state in different laboratories will affect the loss of information.     

Multilayered neural architectures evolution for computing sequences of orthogonal polynomials

This article presents an evolutionary algorithm to autonomously construct full-connected multilayered feedforward neural architectures. This algorithm employs grammar-guided genetic programming with a context-free grammar that has been specifically designed to satisfy three important restrictions. First, the sentences that belong to the language produced by the grammar only encode all valid neural architectures. Second, full-connected feedforward neural architectures of any size can be generated. Third, smaller-sized neural architectures are favored to avoid overfitting. The proposed evolutionary neural architectures construction system is applied to compute the terms of the two sequences that define the three-term recurrence relation associated with a sequence of orthogonal polynomials. This application imposes an important constraint: training datasets are always very small. Therefore, an adequate sized neural architecture has to be evolved to achieve satisfactory results, which are presented in terms of accuracy and size of the evolved neural architectures, and convergence speed of the evolutionary process.     

Transfer learning by centroid pivoted mapping in noisy environment

Transfer learning is a widely investigated learning paradigm that is initially proposed to reuse informative knowledge from related domains, as supervised information in the target domain is scarce while it is sufficiently available in the multiple source domains. One of the challenging issues in transfer learning is how to handle the distribution differences between the source domains and the target domain. Most studies in the research field implicitly assume that data distributions from the source domains and the target domain are similar in a well-designed feature space. However, it is often the case that label assignments for data in the source domains and the target domain are significantly different. Therefore, in reality even if the distribution difference between a source domain and a target domain is reduced, the knowledge from multiple source domains is not well transferred to the target domain unless the label information is carefully considered. In addition, noisy data often emerge in real world applications. Therefore, considering how to handle noisy data in the transfer learning setting is a challenging problem, as noisy data inevitably cause a side effect during the knowledge transfer. Due to the above reasons, in this paper, we are motivated to propose a robust framework against noise in the transfer learning setting. We also explicitly consider the difference in data distributions and label assignments among multiple source domains and the target domain. Experimental results on one synthetic data set, three UCI data sets and one real world text data set in different noise levels demonstrate the effectiveness of our method.     

噪音情况下语音端点检测方法的研究

语音信号处理中减谱法是一种传统的降噪方法,但减谱法利用固定的无音片段作为噪声样本容易产生误差。谱熵法是一种有效的端点检测方法,但在低信噪比环境下,检测效果将大大降低,并且门限估计也采用初始的固定无音片段。为此,提出了一种降噪和端点检测同步的方法。实验结果表明,該方法可以得到较高正确率的端点检测结果。