China

Continuous variable quantum key distribution (CV-QKD) has made great progress recent years. Throughput of error correction is one of the bottlenecks for CV-QKD. For typical distance of CV-QKD system, the signal-to-noise ratio is low, and a large number of iterations is needed for decoder. Since scheduling strategy is an important factor of the decoder’s convergence rate, in this paper, the performance of three different scheduling strategies (flooding scheduling, layered scheduling by row, layered scheduling by column) is compared. The algorithm complexity, adaptation to the parity check matrix, and throughput performance are studied. For the typical parity check matrix for CV-QKD system, the results show that layered scheduling by column is the best method. Throughput of more than 70 Mbits/s is achieved by this method, which surpasses the record in the literature and can improve the real-time processing capacity of CV-QKD system.

Quantum 2020

Study on error correction decoder scheduling strategies for continuous variable quantum key distribution system

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The Institute of Physics and IOP Publishing, in partnership with the Chinese Physical Society (CPS) and the University of Science and Technology of China (USTC) are proud to have brought together the international quantum science community online for this inaugural conference.

Early career and leading researchers from universities, industry and government worldwide came together to share, learn and collaborate on the latest research and emerging areas of high interest across quantum science and technology. 

"HqHop: Hybrid Quantum Hopfield Neural Network for Portfolio Selection

RP Mahajan 
 
Faculty at Dept. of Computer Science & Engineering, SIRT, SAGE University, India
E-mail: rpmahajan.sage@gmail.com ; rpmahajan@yahoo.com

 
Abstract: Classical Hopfield neural networks (CHNN) are known for solving portfolio selection problem for asset management. A Hybrid Quantum Hopfield Neural Network (HqHop) is proposed to solve portfolio selection problem based on Markowitz mean variance approach. Due to the large number of portfolios, the CHNN requires huge memory and processing power. The inbuilt parallelism in CHNN makes it more suitable for quantum based system. This paper details an approach, perhaps the first time, for the solution of portfolio selection problem using the involved concept. The developed HqHop can be used for solving other optimization problems as well.

"

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HqHop: Hybrid Quantum Hopfield Neural Network for Portfolio Selection