A high-fidelity memory scheme for quantum data buses

A novel quantum memory scheme is proposed for quantum data buses in scalable quantum computers by using adjustable interaction. Our investigation focuses on a hybrid quantum system including coupled flux qubits and a nitrogen–vacancy center ensemble. In our scheme, the transmission and storage（retrieval） of quantum state are performed in two separated steps, which can be controlled by adjusting the coupling strength between the computing unit and the quantum memory. The scheme can be used not only to reduce the time of quantum state transmission, but also to increase the robustness of the system with respect to detuning caused by magnetic noises. In comparison with the previous memory scheme, about 80% of the transmission time is saved. Moreover, it is exemplified that in our scheme the fidelity could achieve 0.99 even when there exists detuning, while the one in the previous scheme is 0.75.

Quick System-Level DDR3 Signal Integrity Simulation Research

Double data rate synchronous dynamic random access memory （DDR3） has become one of the most mainstream applications in current server and computer systems. In order to quickly set up a system-level signal integrity （SI） simulation flow for the DDR3 interface, two system-level SI simulation methodologies, which are board-level S-parameter extraction in the frequency-domain and system-level simulation assumptions in the time domain, are introduced in this paper. By comparing the flow of Speed2000 and PowerSI/Hspice, PowerSI is chosen for the printed circuit board （PCB） board-level S-parameter extraction, while Tektronix oscilloscope （TDS7404） is used for the DDR3 waveform measurement. The lab measurement shows good agreement between simulation and measurement. The study shows that the combination of PowerSI and Hspice is recommended for quick system-level DDR3 SI simulation.

Polarization readout analysis for multilevel phase change recording by crystallization degree modulation

Four different states of Si15Sb85 and Ge2Sb2Te5 phase change memory thin films are obtained by crystallization degree modulation through laser initialization at different powers or annealing at different temperatures. The polarization characteristics of these two four-level phase change recording media are analyzed systematically. A simple and effective readout scheme is then proposed, and the readout signal is numerically simulated. The results show that a high-contrast polarization readout can be obtained in an extensive wavelength range for the four-level phase change recording media using common phase change materials. This study will help in-depth understanding of the physical mechanisms and provide technical approaches to multilevel phase change recording.

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

A Partition Checkpoint Strategy Based on Data Segment Priority

A partition checkpoint strategy based on data segment priority is presented to meet the timing constraints of the data and the transaction in embedded real-time main memory database systems（ERTMMDBS） as well as to reduce the number of the transactions missing their deadlines and the recovery time.The partition checkpoint strategy takes into account the characteristics of the data and the transactions associated with it;moreover,it partitions the database according to the data segment priority and sets the corresponding checkpoint frequency to each partition for independent checkpoint operation.The simulation results show that the partition checkpoint strategy decreases the ratio of trans-actions missing their deadlines.

Design and verification of on-chip debug circuit based on JTAG

An on-chip debug circuit based on Joint Test Action Group(JTAG)interface for L-digital signal processor(L-DSP)is proposed,which has debug functions such as storage resource access,central processing unit(CPU)pipeline control,hardware breakpoint/observation point,and parameter statistics.Compared with traditional debug mode,the proposed debug circuit completes direct transmission of data between peripherals and memory by adding data test-direct memory access(DT-DMA)module,which improves debug efficiency greatly.The proposed circuit was designed in a 0.18μm complementary metal-oxide-semiconductor(CMOS)process with an area of 167234.76μm~2 and a power consumption of 8.89 mW.And the proposed debug circuit and L-DSP were verified under a field programmable gate array(FPGA).Experimental results show that the proposed circuit has complete debug functions and the rate of DT-DMA for transferring debug data is three times faster than the CPU.