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A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design

IEEE Transactions on Information Theory 2025年第3期71卷 1803-1823页

作者： Xiao, Shuixin Wang, Yuanlong Zhang, Jun Dong, Daoyi Mooney, Gary J. Petersen, Ian R. Yonezawa, Hidehiro University of New South Wales School of Engineering and Technology CanberraACT2600 Australia University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China The Australian National University School of Engineering CanberraACT2601 Australia Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Australian National University CIICADA Laboratory School of Engineering CanberraACT2601 Australia The University of Melbourne School of Physics ParkvilleVIC3010 Australia RIKEN Center for Quantum Computing Saitama351-0198 Japan Centre for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of O(MLd2) where d is the dimension of the quantum system and M, L(M≥ d2, L≥ d2) represent the numbers of different input states and measurement operators, respectively. We establish an analytical error upper bound and then design the optimal input states and the optimal measurement operators, which are both based on minimizing the error upper bound and maximizing the robustness characterized by the condition number. Numerical examples and testing on IBM quantum devices are presented to demonstrate the performance and efficiency of our algorithm. © 1963-2012 IEEE.

关键词： Error analysis

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Scaling out NUMA-Aware Applications with RDMA-Based Distributed Shared Memory

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Journal of Computer Science & Technology 2019年第1期34卷 94-112页

作者： Yang Hong Yang Zheng Fan Yang Bin-Yu Zang Hai-Bing Guan Hai-Bo Chen Shanghai Key Laboratory for Scalable Computing Systems Shanghai Jiao Tong UniversityShanghai 200240China

The multicore evolution has stimulated renewed interests in scaling up applications on shared-memory multiprocessors,significantly improving the scalability of many *** the scalability is limited within a single node;therefore programmers still have to redesign applications to scale out over multiple *** paper revisits the design and implementation of distributed shared memory (DSM)as a way to scale out applications optimized for non-uniform memory access (NUMA)architecture over a well-connected *** paper presents MAGI,an efficient DSM system that provides a transparent shared address space with scalable performance on a cluster with fast network *** is unique in that it presents a NUMA abstraction to fully harness the multicore resources in each node through hierarchical synchronization and memory *** also exploits the memory access patterns of big-data applications and leverages a set of optimizations for remote direct memory access (RDMA)to reduce the number of page faults and the cost of the coherence *** has been implemented as a user-space library with pthread-compatible interfaces and can run existing multithreaded applications with minimized *** deployed MAGI over an 8-node RDMA-enabled *** evaluation shows that MAGI achieves up to 9.25:4 speedup compared with an unoptimized implementation,leading to a sealable performance for large-scale data-intensive applications.

关键词： distributed shared memory (DSM) scalability multieore evolution non-uniform memory access (NUMA) remote direct memory access (RDMA)

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Optimizations for High Performance Network Virtualization

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Journal of Computer Science & Technology 2016年第1期31卷 107-116页

作者： Fan-Fu Zhou Ru-Hui Ma Jian Li Li-Xia Chen Wei-Dong Qiu Hai-Bing Guan Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai 200240 China

The increasing requirements of intensive interoperaterbility among the distributed nodes desiderate the high performance network connections, owing to the substantial growth of cloud computing and datacenters. Network I/O virtualization aggregates the network resource and separates it into manageable parts for particular servers or devices, which provides effective consolidation and elastic management with high agility, flexibility and scalability as well as reduced cost and cabling. However, both network I/O virtualization aggregation and the increasing network speed incur higher traffic density, which generates a heavy system stress for I/O data moving and I/O event processing. Consequently, many researchers have dedicated to enhancing the system performance and alleviating the system overhead for high performance networking virtualizatiou. This paper first elaborates the mainstreaming I/O virtualization methodologies, including device emulation, split-driver model and hardware assisted model. Then, the paper discusses and compares their specific advantages in addition to performance bottlenecks in practical utilities. This paper mainly focuses on the comprehensive survey of state- of-the-art approaches for performance optimizations and improvements as well as the portability management for network I/O virtualization. The approaches include various novel data delivery schemes, overhead mitigations for interrupt processing and adequate resource allocations for dynamic network states. Finally, we highlight the diversity of I/O virtualization besides the performance improvements in network virtualization infrastructure.

关键词： network virtualization single root I/O virtualization (SR-IOV) virtual machine monitor (VMM)

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Bipartite-Oriented Distributed Graph Partitioning for Big Learning

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Journal of Computer Science & Technology 2015年第1期30卷 20-29页

作者：陈榕施佳鑫陈海波臧斌宇 Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Shanghai 200240 China

Many machine learning and data mining （MLDM] problems like recommendation, topic modeling, and medical diagnosis can be modeled as computing on bipartite graphs. However, inost distributed graph-parallel systems are oblivious to the unique characteristics in such graphs and existing online graph partitioning algorithms usually cause excessive repli- cation of vertices as well as significant pressure on network communication. This article identifies the challenges and oppor- tunities of partitioning bipartite graphs for distributed MLDM processing and proposes BiGraph, a set of bipartite-oriented graph partitioning algorithms. BiGraph leverages observations such as the skewed distribution of vertices, discriminated computation load and imbalanced data sizes between the two subsets of vertices to derive a set of optimal graph partition- ing algorithms that result in minimal vertex replication and network communication. BiGraph has been implemented on PowerGraph and is shown to have a performance boost up to 17.75X （from 1.16X） for four typical MLDM algorithnls, due to reducing up to 80% vertex replication, and up to 96% network traffic.

关键词： bipartite graph graph partitioning graph-parallel system

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Partitioning the Conventional DBT System for Multiprocessors

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Journal of Computer Science & Technology 2011年第3期26卷 474-490页

作者：马汝辉管海兵朱二周杨洪波杨吟冬梁阿磊 Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University

Noticeable performance improvement via ever-increasing transistors is gradually trapped into a predicament since software cannot logically and efficiently utilize hardware resource, such as multi-core resource. This is an inevitable problem in dynamic binary translation （DBT） system as well. Though special purpose hardware as aide tool, through some interfaces, provided by DBT enables the system to achieve higher performance, the limitation of it is significant, that is, it is impossible to be used widely by another one. To overcome this drawback, we focus on building compatible software architecture to acquire higher performance without platform dependence. In this paper, we propose a novel multithreaded architecture for DBT system through partitioning distinct function module, which is to adequately utilize multiprocessors resource. This new architecture devides couples the common DBT system （DBTs） working routine into dynamic translation, optimization, and translated code execution phases, and then ramifies them into different threads to enable them concurrently executed. In this new architecture, several efficient novel methods are presented to cope with intractable work that puzzles most researchers, such as communication mechanism, cache layout, and mutual exclusion between threads. Experimental results using SPECint 2000 indicate that this new architecture for DBT system can achieve higher performance -- speed up the traditional DBT system by about average 10.75%, with better CPU utilization.

关键词： DBT optimization microprocessor multi-core SPECint 2000

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Human-like conceptual representations emerge from language prediction

arXiv

引用

arXiv 2025年

作者： Xu, Ningyu Zhang, Qi Du, Chao Luo, Qiang Qiu, Xipeng Huang, Xuanjing Zhang, Menghan School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China Ministry of Education Key Laboratory of Contemporary Anthropology Fudan University Shanghai China

People acquire concepts through rich physical and social experiences and use them to understand the world. In contrast, large language models (LLMs), trained exclusively through next-token prediction over language data, exhibit remarkably human-like behaviors. Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized? To address these questions, we reframed the classic reverse dictionary task to simulate human concept inference in context and investigated the emergence of human-like conceptual representations within LLMs. Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts. The derived representations converged towards a shared, context-independent structure that effectively predicted human behavior across key psychological phenomena, including computation of similarities, categories and semantic scales. Moreover, these representations aligned well with neural activity patterns in the human brain, even in response to visual rather than linguistic stimuli, providing evidence for biological plausibility. These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding. More broadly, our work positions LLMs as promising computational tools for understanding complex human cognition and paves the way for better alignment between artificial and human intelligence. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Online traffic-aware linked VM placement in cloud data centers

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Science China(Information Sciences) 2020年第7期63卷 182-204页

作者： Liwei LIN David S.L.WEI Ruhui MA Jian LI Haibing GUAN Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Department of Computer & Information Sciences Fordham University

In cloud computing, virtual machine(VM) placement plays a crucial role in data center(DC)management, as different ways of VM placement may require different system resources. As Cisco research reveals that virtualization of DC increases traffic within the DC and causes network bandwidth to become scarce resource, recent researches have been focusing on traffic-aware VM placement. However, previous traffic-aware VM placement schemes treat the VM placement as a static process in that they do not take into account the impact of the current placement decision on the subsequent placement. In this paper,we thus propose a novel online traffic-aware VM placement scheme. Our scheme views VM placement as a context-sensitive dynamic process in that the decision of every step of the placement is made aiming at helping the subsequent steps of placement to reduce the required network bandwidth in the long run. In our scheme, we consider not only inter-VM traffic but also the bandwidth constraint of a physical machine(PM) when making a VM placement decision. To realize our objective, we put those VMs with close end time in the same or close proximity PMs so that when the VMs are terminated, one can make enough room for the future arrivals so as to not only minimize the number of active PMs but also reduce networking costs. We conduct extensive simulations to verify the superiority of our scheme in terms of networking costs and energy consumption. Simulation results show that our scheme outperforms improved-best-fit-decreasing(IBFD) scheme, a revised best-fit version that takes inter-VM traffic into account, by 30%–40% on network cost under various scenarios. Our scheme also promises 10%–25% power savings compared with IBFD.

关键词： linked VMs traffic-aware online VM placement cloud data center energy efficient

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PAM: an efficient power-aware multilevel cache policy to reduce energy consumption of storage systems

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Frontiers of Computer Science 2019年第4期13卷 850-863页

作者： Xiaodong MENG Chentao WU Minyi GUO Long ZHENG Jingyu ZHANG Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science & Engineering Shanghai Jiao Tong University Shanghai 200240 China

Energy consumption is one of the most significant aspects of large-scale storage systems where multilevel caches are widely used. In a typical hierarchical storage structure, upper-level storage serves as a cache for the lower level, forming a distributed multilevel cache system. In the past two decades, several classic LRU-based multilevel cache policies have been proposed to improve the overall I/O performance of storage systems. However, few power-aware multilevel cache policies focus on the storage devices in the bottom level, which consume more than 27% of the energy of the whole system [1]. To address this problem, we propose a novel power-aware multilevel cache (PAM) policy that can reduce the energy consumption of high-performance and I/O bandwidth storage devices. In our PAM policy, an appropriate number of cold dirty blocks in the upper level cache are identified and selected to flush directly to the storage devices, providing high probability extension of the lifetime of disks in standby mode. To demonstrate the effectiveness of our proposed policy, we conduct several simulations with real-world traces. Compared to existing popular cache schemes such as PA? LRU, PB-LRU, and Demote, PAM reduces power consumption by up to 15% under different I/O workloads, and improves energy efficiency by up to 50.5%.

关键词： storage system multilevel cache energy consumption I/O performance hint

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Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges

arXiv

引用

arXiv 2025年

作者： Zhou, Xiaotian Zehmakan, Ahad N. Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China School of Computing Australian National University Canberra Australia

The Kirchhoff index, which is the sum of the resistance distance between every pair of nodes in a network, is a key metric for gauging network performance, where lower values signify enhanced performance. In this paper, we study the problem of minimizing the Kirchhoff index by adding edges. We first provide a greedy algorithm for solving this problem and give an analysis of its quality based on the bounds of the submodularity ratio and the curvature. Then, we introduce a gradient-based greedy algorithm as a new paradigm to solve this problem. To accelerate the computation cost, we leverage geometric properties, convex hull approximation, and approximation of the projected coordinate of each point. To further improve this algorithm, we use pre-pruning and fast update techniques, making it particularly suitable for large networks. Our proposed algorithms have nearly-linear time complexity. We provide extensive experiments on ten real networks to evaluate the quality of our algorithms. The results demonstrate that our proposed algorithms outperform the state-of-the-art methods in terms of efficiency and effectiveness. Moreover, our algorithms are scalable to large graphs with over 5 million nodes and 12 million edges. Copyright © 2025, The Authors. All rights reserved.

关键词： Consensus algorithm

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An efficient adapting virtual intermediate instruction set towards optimized dynamic binary translator (DBT) system

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Journal of Central South University 2012年第11期19卷 3118-3128页

作者：杨吟冬管海兵 Shanghai Key Laboratory of Scalable Computing and Systems(Shanghai Jiao Tong University)

A new efficient adapting virtual intermediate instruction set,V-IIS,is designed and implemented towards the optimized dynamic binary translator (DBT) *** the help of this powerful but previously little-studied component,DBTs can not only get rid of the dependence of machine(s),but also get better *** our systematical study and evaluation,experimental results demonstrate that if V-IIS is well designed,without affecting the other optimizing measures,this could make DBT's performance close to those who do not have intermediate *** study is an important step towards the grand goal of high performance "multi-source" and "multi-target" dynamic binary translation.

关键词： binary translation virtual intermediate instruction set dynamic binary translator (DBT)

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