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arXiv 2024年

作者： Cai, Yishuai Yang, Shaowu Li, Minglong Chen, Xinglin Mao, Yunxin Yi, Xiaodong Yang, Wenjing The Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

Optimizing the morphologies and the controllers that adapt to various tasks is a critical issue in the field of robot design, aka. embodied intelligence. Previous works typically model it as a joint optimization problem and use search-based methods to find the optimal solution in the morphology space. However, they ignore the implicit knowledge of task-to-morphology mapping which can directly inspire robot design. For example, flipping heavier boxes tends to require more muscular robot arms. This paper proposes a novel and general differentiable task-inspired framework for contact-aware robot design called Task2Morph. We abstract task features highly related to task performance and use them to build a task-to-morphology mapping. Further, we embed the mapping into a differentiable robot design process, where the gradient information is leveraged for both the mapping learning and the whole optimization. The experiments are conducted on three scenarios, and the results validate that Task2Morph outperforms DiffHand, which lacks a task-inspired morphology module, in terms of efficiency and effectiveness. Copyright © 2024, The Authors. All rights reserved.

关键词： Morphology

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AutoSketch: automatic sketch-oriented compiler for query-driven network telemetry 24

AutoSketch: automatic sketch-oriented compiler for query-dri...

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Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation

作者： Haifeng Sun Qun Huang Jinbo Sun Wei Wang Jiaheng Li Fuliang Li Yungang Bao Xin Yao Gong Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Institute of Computing Technology CAS Northeastern University China Huawei Theory Department

ISBN: (纸本)9781939133397

Recent network telemetry witnesses tremendous progress in two directions: query-driven telemetry that targets expressiveness as the primary goal, and sketch-based algorithms that address resource-accuracy trade-offs. In this paper, we propose AutoSketch that aims to integrate the advantages of both classes. In a nutshell, AutoSketch automatically compiles high-level operators into sketch instances that can be readily deployed with low resource usage and incur limited accuracy drop. However, there remains a gap between the expressiveness of high-level operators and the underlying realization of sketch algorithms. AutoSketch bridges this gap in three aspects. First, AutoSketch extends its interface derived from existing query-driven telemetry such that users can specify the desired telemetry accuracy. The specified accuracy intent will be utilized to guide the compiling procedure. Second, AutoSketch leverages various techniques, such as syntax analysis and performance estimation, to construct efficient sketch instances. Finally, AutoSketch automatically searches for the most suitable parameter configurations that fulfill the accuracy intent with minimum resource usage. Our experiments demonstrate that AutoSketch can achieve high expressiveness, high accuracy, and low resource usage compared to state-of-the-art telemetry solutions.

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On the Instance Optimality of Detecting Collisions and Subgraphs

arXiv

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arXiv 2023年

作者： Ben-Eliezer, Omri Grossman, Tomer Naor, Moni Simons Institute for the Theory of Computing University of California Berkeley United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel

Suppose you are given a function f : [n] → [n] via (black-box) query access to the function. You are looking to find something local, like a collision (a pair x ≠ y s.t. f(x) = f(y)). The question is whether knowing the ‘shape’ of the function helps you or not (by shape we mean that some permutation of the function is known). Formally, we investigate the unlabeled instance optimality of substructure detection problems in graphs and functions. A problem is g(n)-instance optimal if it admits an algorithm A satisfying that for any possible input, the (randomized) query complexity of A is at most g(n) times larger than the query complexity of any algorithm A′ which solves the same problem while holding an unlabeled copy of the input (i.e., any A′ that "knows the structure of the input"). Our results point to a trichotomy of unlabeled instance optimality among substructure detection problems in graphs and functions: • A few very simple properties have an O(1)-instance optimal algorithm. • Most properties of graphs and functions, with examples such as containing a fixed point or a 3-collision in functions, or a triangle in graphs, are nΩ(1)-far from instance optimality. • The problems of collision detection in functions and finding a claw in a graph serve as a middle ground between the two regimes. We show that these two properties are Ω(log n)far from instance optimality, and conjecture that this bound is tight. We provide evidence towards this conjecture, by proving that finding a claw in a graph is O(log(n))-instance optimal among all input graphs for which the query complexity of an algorithm holding an unlabeled certificate is (formula presanted) . Copyright © 2023, The Authors. All rights reserved.

关键词： Optimal detection

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quantum and classical query complexities for determining connectedness of matroids

arXiv

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arXiv 2023年

作者： Huang, Xiaowei Feng, Shiguang Li, Lvzhou Institute of Quantum Computing and and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

Connectivity is a fundamental structural property of matroids, and has been studied algorithmically over 50 years. In 1974, Cunningham proposed a deterministic algorithm consuming O(n2) queries to the independence oracle to determine whether a matroid is connected. Since then, no algorithm, not even a random one, has worked better. To the best of our knowledge, the classical query complexity lower bound and the quantum complexity for this problem have not been considered. Thus, in this paper we are devoted to addressing these issues, and our contributions are threefold as follows: (i) First, we prove that the randomized query complexity of determining whether a matroid is connected is Ω(n2) and thus the algorithm proposed by Cunningham is optimal in classical computing. (ii) Second, we present a quantum algorithm with O(n3/2) queries, which exhibits provable quantum speedups over classical ones. (iii) Third, we prove that any quantum algorithm requires Ω(n) queries, which indicates that quantum algorithms can achieve at most a quadratic speedup over classical ones. Therefore, we have a relatively comprehensive understanding of the potential of quantum computing in determining the connectedness of matroids. © 2023, CC BY.

关键词： quantum theory

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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The 20-nm Skyrmion Generated at Room Temperature by Spin-Orbit Torques

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Chinese Physics Letters 2022年第1期39卷 40-45页

作者： Jiahao Liu Zidong Wang Teng Xu Hengan Zhou Le Zhao Soong-Guen Je Mi-Young Im Liang Fang Wanjun Jiang State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Frontier Science Center for Quantum Information Tsinghua UniversityBeijing 100084China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Center for X-ray Optics Lawrence Berkeley National LaboratoryCyclotron RoadBerkeleyCA 94720USA Department of Physics Chonnam National UniversityGwangju61186Republic of Korea

The discovery of magnetic skyrmions provides a promising pathway for developing functional spintronic memory and logic devices. Towards the future high-density memory application, nanoscale skyrmions with miniaturized diameters, ideally down to 20 nm are required. Using x-ray magnetic circular dichroism transmission microscopy,nanoscale skyrmions are observed in the [Pt/Co/Ir]15 multilayer at room temperature. In particular, small skyrmions with minimum diameters approaching 20 nm could be generated by the current-induced spin-orbit torques. Through implementing material specific parameters, the dynamic process of skyrmion generation is further investigated by performing micromagnetic simulations. According to the simulation results, we find that both the tube-like Neel-type skyrmions and the bobber-like Neel-type skyrmions can be electrically generated. In particular, the size of the bobber-like Neel-type skyrmions can be effectively reduced by the spin-orbit torques,which leads to the formation of 20 nm Neel-type skyrmions. Our findings could be important for understanding the formation dynamics of nanoscale Neel-type spin textures, skyrmions and bobber in particular, which could also be useful for promoting nanoscale skyrmionic memories and logic devices.

关键词： temperature diameters approaching

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Experimental demonstration of quantum transport enhancement using time-reversal symmetry breaking on a silicon photonic chip

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science China(Physics,Mechanics & Astronomy) 2022年第10期65卷 2-9页

作者： Yang Wang Xinyao Yu Shichuan Xue Yizhi Wang Junwei Zhan Chao Wu Pingyu Zhu Qilin Zheng Miaomiao Yu Yingwen Liu Xiaogang Qiang Junjie Wu Xuejun Yang Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology Academy of Military SciencesBeijing 100071China National Laboratory of Solid State Microstructures and School of Physics Nanjing UniversityNanjing 210093China

The continuous-time quantum walk is a basic model for studying quantum transport and developing quantum-enhanced algorithms. Recent studies show that by introducing a phase into the standard continuous-time quantum walk model, the time-reversal symmetry can be broken without changing the Hermitian property of the Hamiltonian. The time-reversal symmetry breaking quantum walk shows advantages in quantum transport, such as perfect state transfer, directional control, transport speedup, and quantum transport efficiency enhancement. In this work, we implement the time-reversal symmetry breaking quantum walks on a reconfigurable silicon photonic chip and demonstrate the enhancement introduced by breaking time-reversal symmetry. Perfect state transfer on a three-site ring, a quantum switch implemented on a six-site graph, and transport speedup using a linear chain of triangles are demonstrated with high fidelity. Time-reversal asymmetry has also been used in a simplified light-harvesting model,implying the potential of time-reversal symmetry breaking in photosynthesis investigations.

关键词： quantum walk time-reversal asymmetry quantum transport

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QSCL-EWIL: quantum Stochastic Contrast Learning for Enhanced WiFi-Based Indoor Localization

arXiv

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arXiv 2025年

作者： Dastagir, Muhammad Bilal Akram Han, Dongsoo Al-Kuwari, Saif Farouk, Ahmed The School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar The Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt

WiFi-based indoor localization is essential for asset tracking, healthcare monitoring, and smart buildings. However, existing systems face challenges such as data variability, environmental noise, and difficulty detecting floor and building levels, compounded by limited labeled data and high received signal strength (RSS) collection costs. This paper introduces quantum stochastic contrast learning (QSCL), a novel framework grounded in rigorous theoretical foundations. We present four theorems and one lemma that establish the probabilistic augmentation, diversity enhancement, relationship preservation, and resilience of QSCL under quantum noise, supported by formal proofs. Leveraging these foundations, QSCL utilizes quantum computing (QC) to generate strong data augmentations with stochastic perturbations, enhancing data diversity, while classical weak augmentations provide subtle variations for robust feature learning. We propose a spatial temporal adaptive attention (STAA) encoder that integrates convolutional layers with adaptive attention mechanisms to capture spatial and temporal dependencies in sequential data. Furthermore, a bidirectional contrastive loss function is introduced to capture forward and reverse relationships between augmented views, ensuring robust representations. Comprehensive evaluations on the UJIIndoorLoc and UTSIndoorLoc datasets validate QSCL, demonstrating superior performance with reduced labeled data and resilience to quantum noise such as bit-flip, dephasing, and measurement noise. The proposed framework significantly improves localization accuracy, floor and building detection, and generalizability in challenging indoor environments. © 2025, CC BY.

关键词： quantum noise

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quantum Enhanced Simulation Based Optimization for Newsvendor Problems

Quantum Enhanced Simulation Based Optimization for Newsvendo...

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quantum computing and Engineering (QCE), IEEE International Conference on

作者： Monit Sharma Hoong Chuin Lau Rudy Raymond School of Computing and Information Systems Singapore Management University Singapore Institute of High Performance Computing A*STAR Singapore IBM Quantum IBM Japan Japan Dept. of Computer Science The University of Tokyo

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence is estimated through simulation. Exploiting the enhanced efficiency of quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we use a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical Newsvendor Problem known to be NP-hard. Such problems provide the building block for supply chain management, particularly in inventory management and procurement optimization under risks and uncertainty.

关键词： Procurement quantum computing Uncertainty Monte Carlo methods Inventory management Linear programming Amplitude estimation Object recognition Computational complexity Optimization

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Non-Classical quantum State Generation using Superconducting-Nanostrip Photon-Number Resolving Detector with Optical Sampling

Non-Classical Quantum State Generation using Superconducting...

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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： quantum computers

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