Bidirectional Information Flow Quantum State Tomography

The exact reconstruction of many-body quantum systems is one of the major challenges in modern physics,because it is impractical to overcome the exponential complexity problem brought by high-dimensional quantum manybody systems.Recently,machine learning techniques are well used to promote quantum information research and quantum state tomography has also been developed by neural network generative models.We propose a quantum state tomography method,which is based on a bidirectional gated recurrent unit neural network,to learn and reconstruct both easy quantum states and hard quantum states in this study.We are able to use fewer measurement samples in our method to reconstruct these quantum states and to obtain high fidelity.

A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

As the power demand in data centers is increasing,the power capacity of the power supply system has become an essential resource to be optimized.Although many data centers use power oversubscription to make full use of the power capacity,there are unavoidable power supply risks associated with it.Therefore,how to improve the data center power capacity utilization while ensuring power supply security has become an important issue.To solve this problem,we first define it and propose a risk evaluation metric called Weighted Power Supply Risk(WPSRisk).Then,a method,named Hybrid Genetic Algorithm with Ant Colony System(HGAACS),is proposed to improve power capacity utilization and reduce power supply risks by optimizing the server placement in the power supply system.HGAACS uses historical power data of each server to find a better placement solution by population iteration.HGAACS possesses not only the remarkable local search ability of Ant Colony System(ACS),but also enhances the global search capability by incorporating genetic operators from Genetic Algorithm(GA).To verify the performance of HGAACS,we experimentally compare it with five other placement algorithms.The experimental results show that HGAACS can perform better than other algorithms in both improving power utilization and reducing the riskof powersupply system.