Chenbin Zhang

Incoherent control of locally controllable quantum systems

An incoherent control scheme for state control of locally controllable quantum systems is proposed. This scheme includes three steps: (1) amplitude amplification of the initial state by a suitable unitary transformation, (2) projective measurement of the amplified state, and (3) final optimization by a unitary controlled transformation. The first step increases the amplitudes of some desired eigenstates and the corresponding probability of observing these eigenstates, the second step projects, with high probability, the amplified state into a desired eigenstate, and the last step steers this eigenstate into the target state. Within this scheme, two control algorithms are presented for two classes of quantum systems. As an example, the incoherent control scheme is applied to the control of a hydrogen atom by an external field. The results support the suggestion that projective measurements can serve as an effective control and local controllability information can be used to design control laws for quantum systems. Thus, this scheme establishes a subtle connection between control design and controllability analysis of quantum systems and provides an effective engineering approach in controlling quantum systems with partial controllability information.

Control of non-controllable quantum systems: a quantum control algorithm based on Grover iteration

A new notion of controllability, eigenstate controllability, is defined for finite-dimensional bilinear quantum mechanical systems which are neither strongly completely controllable nor completely controllable. Moreover, a quantum control algorithm based on Grover iteration is designed to perform a quantum control task of steering a system, which is eigenstate controllable but may not be (strongly) completely controllable, from an arbitrary state to a target state.

Fast object detection based on selective visual attention

Selective visual attention plays an important role in human visual system. In real life, human visual system cannot handle all of the visual information captured by eyes on time. Selective visual attention filters the visual information and selects interesting one for further processing such as object detection. Inspired by this mechanism, we construct an object detection method which can speed up the object detection relative to the methods that search objects by using sliding window. This method firstly extracts saliency map from the origin image, and then gets the candidate detection area from the saliency map by adaptive thresholds. To detect object, we only need to search the candidate detection area with the deformable part model. Since the candidate detection area is much smaller than the whole image, we can speed up the object detection. We evaluate the detection performance of our approach on PASCAL 2008 dataset, INRIA person dataset and Caltech 101 dataset, and the results indicate that our method can speed up the detection without decline in detection accuracy.

Global feature integration based salient region detection

Abstract The goal of saliency detection is to locate the regions which are most likely to capture human׳s attention without prior knowledge of their contents. Visual saliency detection has been widely used in image processing, but it is still a challenging problem in computer vision. In this paper, we propose a salient region detection algorithm by integrating global features namely uniqueness and spatial distribution. Two measures of contrast are computed in pixel and superpixel level respectively. In order to suppress background noise, Low-level features are refined by High-level priors which are computed with the Gaussian model based on salient region. We formulate salient region detection as a binary labeling problem that separates salient region from the background. A Conditional Random Field is learned to effectively combine these refined features for salient region detection. Experimental results on the large benchmark database demonstrate the proposed method performs well when against fifteen state-of-the-art methods in terms of precision and recall.

An autonomous mobile robot based on quantum algorithm

In this paper, we design a novel autonomous mobile robot which uses quantum sensors to detect faint signals and fulfills some learning tasks using quantum reinforcement learning (QRL) algorithms. In this robot, a multi-sensor system is designed with SQUID sensor and quantum Hall sensor, where quantum sensors coexist with traditional sensors. A novel QRL algorithm is applied and a simple simulation example demonstrates its validity.

QUANTUM FEEDBACK CONTROL USING QUANTUM CLONING AND STATE RECOGNITION

Abstract A scheme of quantum feedback control with an optimal cloning machine is proposed. The design of quantum feedback control algorithms is separated into a state recognition strategy, which gives “on-off” signal to the actuator through recognizing some copies from cloning machine, and a feedback (control) strategy through feeding back the another copies of cloning machine. Precise feedback is abandoned and a compromise between information acquisition and measurement disturbance is established. The recognition process involves measurement and is destructive, however, the feedback step without measurement is preserving quantum coherence, so the scheme can perform some quantum control tasks with coherent feedback.

Predicting salient object via multi-level features

A wide variety of methods have been developed to predict where people look in natural scenes focused on pixel-level image attributes. Most existing methods measure the saliency of a pixel or region based on its contrast within a local context or the entire image. In this paper, we propose a novel salient object detection algorithm by integrating multi-level features including local contrast, global contrast, and background priors which measure the visual saliency in pixel-level, region-level, and object-level. We use the low level visual cues based on the convex hull to separate salient object from the background. The background priors are computed from the background templates using Principal Component Analysis. In order to suppress background noise, local and global contrasts are refined by object center priors which are computed with the Gaussian model based on background priors. Experimental results on four widely used public benchmark datasets demonstrate the proposed method performs well when against fifteen state-of-the-art methods in terms of precision and recall. We also demonstrate Otsu adaptive threshold method can be used to create high quality segmentation masks. Combine multi-level features including local contrast, global contrast, and background priors which measure the visual saliency in pixel-level, region-level, and object-level.We use the low level visual cues based on the convex hull to separate salient object from the background. The background priors of object are computed from the background templates using PCA.In order to suppress background noise, local and global contrasts are refined by object center priors which are computed with the Gaussian model based on background priors.

A novel lithium-ion battery model for state of charge estimation under dynamic currents

An accurate battery model is one of the most important factors to improve the capability of battery state of charge (SoC) estimation. In this paper, battery hysteresis behaviors under different SoC are considered to decrease battery model error, and the hysteresis voltage based battery model (HVBBM) is presented. The experiment result shows that this model can describe the battery discharging process accurately under dynamic current conditions. A method of the adaptive extended Kalman filter (AEKF) based on HVBBM is applied to estimate battery SoC since AEKF can update the process and measurement noise covariances adaptively during the estimation. The comparison results indicate that the method proposed in this paper can improve SoC estimation accuracy under dynamic currents.

QUANTUM CONTROL BASED ON QUANTUM INFORMATION

Quantum control strategy is discussed from the perspective of quantum information. First, the constraints imposed on quantum control by quantum theory are analyzed. Then some quantum control schemes based on quantum information are discussed, such as teleportation-based distant quantum control, quantum feedback control using quantum cloning and state recognition, quantum control based on measurement and Grover iteration. Finally, some applications of quantum control theory in quantum information and quantum computation such as quantum error correction coding, universality analysis of quantum computation, feedback-induced entanglement enhancement, etc., are presented and the potential applications of quantum control are also prospected.

Control of Five-qubit System Based on Quantum Reinforcement Learning

Controlling the multi-qubit system is a key task for practical quantum information processing. In this paper, the control problem of five-qubit is studied. A novel quantum reinforcement learning algorithm based on quantum superposition principle is proposed for the quantum control problem. The simulated result shows that quantum reinforcement learning can effectively find the optimal control sequence through fast learning