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Reducing the CNOT Count for Clifford plus T Circuits on NISQ Architectures

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2023年第6期42卷 1873-1884页

作者： Gheorghiu, Vlad Huang, Jiaxin Li, Sarah Meng Mosca, Michele Mukhopadhyay, Priyanka Univ Waterloo Inst Quantum Comp Waterloo ON N2L 3G1 Canada SoftwareQ Inc Kitchener ON N2H 5G5 Canada Univ Waterloo Fac Math Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Combinator & Optimizat Waterloo ON N2L 3G1 Canada Perimeter Inst Theoret Phys Waterloo ON N2L 2Y5 Canada

While mapping a quantum circuit to the physical layer one has to consider the numerous constraints imposed by the underlying hardware architecture. Connectivity of the physical qubits is one such constraint that restricts two-qubit operations, such as CNOT, to "connected" qubits. SWAP gates can be used to place the logical qubits on admissible physical qubits, but they entail a significant increase in CNOT-count. In this article, we consider the problem of reducing the CNOT-count in Clifford+T circuits on connectivity-constrained architectures, like noisy intermediate-scale quantum (NISQ) computing devices. We "slice" the circuit at the position of Hadamard gates and "build" the intermediate {CNOT, T} subcircuits using Steiner trees, significantly improving on previous methods. We com-pared the performance of our algorithms while mapping different benchmark and random circuits to some well-known architec-tures, such as 9-qubit square grid, 16-qubit square grid, Rigetti 16-qubit Aspen, 16-qubit IBM QX5, and 20-qubit IBM Tokyo. Our methods give less CNOT-count compared to Qiskit and TKET transpiler as well as using SWAP gates. Assuming most of the errors in an NISQ circuit implementation are due to CNOT errors, then our method would allow circuits with a few times more CNOT gates be reliably implemented than the previous methods would permit.

关键词： Logic gates Qubit Steiner trees Computer architecture partitioning algorithms Quantum circuit Hardware CNOT-count connectivity constraints noisy intermediate-scale quantum (NISQ) architectures Steiner tree

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Joint Optimization of DNN Partition and Continuous Task Scheduling for Digital Twin-Aided MEC Network With Deep Reinforcement Learning

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IEEE ACCESS 2023年 11卷 27099-27110页

作者： Yuan, Siyu Zhang, Zhenyu Li, Qin Li, Weiyuan Zhang, Yong Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Work Safety Intelligent Monitoring Beijing 100876 Peoples R China China Mobile Res Inst Beijing 100032 Peoples R China

Nowadays, mobile application services face the challenges of high speed, low latency and high reliability. The combination of digital twin (DT) technology and mobile edge computing (MEC) network can effectively solve these challenges. DT technology can help MEC network monitor and predict the network states. In this paper, we propose a DT-aided MEC network scenario with deep neural network (DNN) inference as the computing task of end devices (EDs). ED can offload part of DNN layers to MEC server. To allocate communication resources, we propose an algorithm based on asynchronous advantage actor-critic (A3C), which manages the transmission power and channel selection of EDs. Since DNN inference is continuous in real scenes, we consider the continuous DNN inference tasks. We convert the DNN optimal partition point solving problem to a min st-cut problem, and propose a graph theory based DNN optimal partition point solving algorithm to minimize the inference latency. Simulation results show that the proposed algorithm can effectively reduce the inference latency. Compared with actor-critic (AC) and deep Q network (DQN), the proposed algorithm has faster convergence speed and better convergence value. Compared with the traditional one-time DNN model partition algorithm, the proposed algorithm is more suitable for DNN continuous task arrival scenario.

关键词： Task analysis Servers partitioning algorithms Inference algorithms Resource management Delays Computational modeling DT MEC network continuous DNN inference task A3C min st-cut

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Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第6期8卷 3534-3541页

作者： Gugliermo, Simona Schaffernicht, Erik Koniaris, Christos Pecora, Federico Orebro Univ Ctr Appl Autonomous Sensor Syst S-70182 Orebro Sweden Scan CV AB Intelligent Transport Syst S-15187 Sodertalje Sweden

The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.

关键词： Behavioral sciences Decision trees Planning Batteries Task analysis Circuit synthesis partitioning algorithms Behavior-based systems intelligent transportation systems learning from demonstration

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Semi-Supervised Clustering Under a "Compact-Cluster" Assumption

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第5期35卷 5244-5256页

作者： Jiang, Zhen Zhan, Yongzhao Mao, Qirong Du, Yang Jiangsu Univ Zhenjiang 212013 Jiangsu Peoples R China

Semi-supervised clustering (SSC) aims to improve clustering performance with the support of prior knowledge (i.e., side information). Compared with pairwise constraints, the partial labeling information is more natural to characterize the data distribution in a high level. However, the natural gap between the class information and the clustering is not adequately taken into account in exiting SSC methods when utilizing partial labeling information to guide the clustering procedure. In order to address this problem, we present a "compact-cluster" assumption for SSC to utilize the partial labeling information via a cluster-splitting technique. Based on this assumption, a general framework, CSSC, is proposed to supervise the traditional clustering with an objective function which is defined by incorporating an item to measure the compact degree of clusters. Furthermore, we provide two effective solutions for Kmeans and spectral clustering within the CSSC framework and derive the corresponding algorithms to seek the optimum number of clusters and their centroids. Corresponding theoretical analyses demonstrate the feasibility and effectivity of the proposed method. Finally, the extensive experiments on eight real-world datasets demonstrate the superiority of our method over other state-of-the-art SSC methods.

关键词： Clustering algorithms Labeling Linear programming partitioning algorithms Search problems Optimization Transforms Semi-supervised clustering partial labeling compact-cluster assumption cluster-splitting

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iCVI-ARTMAP: Using Incremental Cluster Validity Indices and Adaptive Resonance Theory Reset Mechanism to Accelerate Validation and Achieve Multiprototype Unsupervised Representations

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第12期34卷 9757-9770页

作者： da Silva, L. E. Brito Rayapati, Nagasharath Wunsch, Donald C., II Guise AI Inc Rolla MO 65401 USA Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA

This article presents an adaptive resonance theory predictive mapping (ARTMAP) model, which uses incremental cluster validity indices (iCVIs) to perform unsupervised learning, namely, iCVI-ARTMAP. Incorporating iCVIs to the decision-making and many-to-one mapping capabilities of this adaptive resonance theory (ART)-based model can improve the choices of clusters to which samples are incrementally assigned. These improvements are accomplished by intelligently performing the operations of swapping sample assignments between clusters, splitting and merging clusters, and caching the values of variables when iCVI values need to be recomputed. Using recursive formulations enables iCVI-ARTMAP to considerably reduce the computational burden associated with cluster validity index (CVI)-based offline clustering. In this work, six iCVI-ARTMAP variants were realized via the integration of one information-theoretic and five sum-of-squares-based iCVIs into fuzzy ARTMAP. With proper choice of iCVI, iCVI-ARTMAP either outperformed or performed comparably to three ART-based and four non-ART-based clustering algorithms in experiments using benchmark datasets of different natures. Naturally, the performance of iCVI-ARTMAP is subject to the selected iCVI and its suitability to the data at hand;fortunately, it is a general model in which other iCVIs can be easily embedded.

关键词： Subspace constraints Clustering algorithms Merging Adaptation models partitioning algorithms Prototypes Machine learning algorithms Adaptive resonance theory (ART) adaptive resonance theory predictive mapping (ARTMAP) clustering incremental cluster validity indices (iCVIs) validation

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Tree Recovery by Dynamic Programming

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 15870-15882页

作者： Gratacos, Gustavo Chakrabarti, Ayan Ju, Tao Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA Google Res New York NY 10011 USA

Tree-like structures are common, naturally occurring objects that are of interest to many fields of study, such as plant science and biomedicine. Analysis of these structures is typically based on skeletons extracted from captured data, which often contain spurious cycles that need to be removed. We propose a dynamic programming algorithm for solving the NP-hard tree recovery problem formulated by (Estrada et al. 2015), which seeks a least-cost partitioning of the graph nodes that yields a directed tree. Our algorithm finds the optimal solution by iteratively contracting the graph via node-merging until the problem can be trivially solved. By carefully designing the merging sequence, our algorithm can efficiently recover optimal trees for many real-world data where (Estrada et al. 2015) only produces sub-optimal solutions. We also propose an approximate variant of dynamic programming using beam search, which can process graphs containing thousands of cycles with significantly improved optimality and efficiency compared with (Estrada et al. 2015).

关键词： partitioning algorithms Heuristic algorithms Skeleton Dynamic programming Shape Merging Costs Tree recovery dynamic programming carving decomposition beam search

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A New Density Peak Clustering Algorithm With Adaptive Clustering Center Based on Differential Privacy

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IEEE ACCESS 2023年 11卷 1418-1431页

作者： Chen, Hua Zhou, Yuan Mei, Kehui Wang, Nan Cai, Guangxing Hubei Univ Technol Sch Sci Wuhan 430068 Peoples R China

A new density peak clustering (DPC) algorithm with adaptive clustering center based on differential privacy was proposed to solve the problems of poor adaptability of high-dimensional data, inability to automatically determine clustering centers, and privacy problems in clustering analysis. First, to solve the problem of poor adaptability of high-dimensional data, cosine distance was used to measure the similarity between high-dimensional datasets. Then, aiming at the subjective problem of clustering center selection, from the perspective of ranking graph, the weight (i - 1)/i was introduced creatively, the slope trend of ranking graph was redefined to realize the adaptive clustering center. Finally, aiming at the privacy problem, the Laplacian noise of appropriate privacy budget was added to the core statistic (local density) of the algorithm to achieve the balance between privacy protection and algorithm effectiveness. Experimental results on both the synthetic and UCI datasets show that this algorithm can not only realize the automatic selection of clustering center, but also solve the privacy problem in clustering analysis, and improve the clustering evaluation index greatly, which proves the effectiveness of the algorithm.

关键词： Clustering algorithms partitioning algorithms Privacy Noise measurement Laplace equations Differential privacy Statistical analysis Cosine distance differential privacy DPC algorithm Laplacian noise trend of slope change

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Neural Acceleration of Graph Based Utility Functions for Sparse Matrices

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IEEE ACCESS 2023年 11卷 31619-31635页

作者： Booth, Joshua Dennis Bolet, Gregory S. Univ Alabama Huntsville Dept Comp Sci Huntsville AL 35899 USA Virginia Tech Blacksburg VA 24061 USA

Many graph-based algorithms in high performance computing (HPC) use approximate solutions due to having algorithms that are computationally expensive or serial in nature. Neural acceleration, i.e., the process of speeding up approximation computation elements via artificial neural networks, is relatively new and has not focused on HPC graph-based algorithms. In this paper, we propose a starting point for applying models for neural acceleration to graph-based HPC algorithms utilizing an understanding of the connectivity computational pattern, recursive neural networks, and graph neural networks. We demonstrate these techniques on the problem related to the utility functions of sparse matrix ordering and fill-in (i.e., zero elements becoming nonzero during factorization) calculations. The problem of sparse matrix ordering is commonly used for issues related to load balancing, improving memory reuse, or reducing computational and memory costs in direct sparse linear solver methods. These utility functions are ideal for demonstration as they comprise a number of different graph-based subproblems, and thus demonstrate the usefulness of our method over a wide range. We show that we can accurately approximate the best ordering and the nonzero count of the sparse factorization matrix while speeding up the calculation by as much as 30.3x over the traditional serial method.

关键词： Sparse matrices Approximation algorithms Hardware Computational modeling partitioning algorithms Runtime Artificial neural networks Approximate computing high performance computing linear algebra sparse matrices machine learning algorithms neural networks

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Weak-Boundary Sensitive Superpixel Segmentation Based on Local Adaptive Distance

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第5期33卷 2302-2316页

作者： Sun, Limin Ma, Dongyang Pan, Xiao Zhou, Yuanfeng Shandong Univ Sch Software Jinan 250101 Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Shandong Key Lab Digital Media Technol Jinan 250100 Peoples R China

Superpixel segmentation provides a way to capture object boundaries unsupervised and has benefited many compute vision applications. However, under-segmentation for weak boundaries and poor compatibility with image feature representations often limit its wide application. In this paper, we propose a new weak-boundary sensitive superpixel generation method and provide an all-in-one solution for images with different feature representations. We first design a local adaptive distance (LAD) to be more sensitive to feature changes in low-contrast regions. LAD leverages image local standard deviation as region contrast clues. It adaptively increases the feature distances in low-contrast regions to avoid feature space distances of weak boundaries being inundated by regularity constraints. LAD is scale-invariant that can be compatible with high bit-depth and multi-feature images. Then, based on LAD, we introduce a novel morphological contour evolution model to generate superpixels iteratively. Leveraging morphological dilation of superpixel shapes, the new model is more conducive to the boundary detection of irregular or slender objects. Extensive experiments demonstrate that our method favorably outperforms state-of-the-art methods, especially regarding the under-segmentation error and segmentation accuracy.

关键词： Image segmentation Clustering algorithms Feature extraction Standards Task analysis partitioning algorithms Image edge detection Superpixels image segmentation weak boundary local adaptive distance multi-feature morphology dilation

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Late Fusion Multiple Kernel Clustering With Proxy Graph Refinement

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 4359-4370页

作者： Wang, Siwei Liu, Xinwang Liu, Li Zhou, Sihang Zhu, En Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 90014 Finland Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to improve clustering performance. Among existing MKC algorithms, the recently proposed late fusion MKC methods demonstrate promising clustering performance in various applications and enjoy considerable computational acceleration. However, we observe that the kernel partition learning and late fusion processes are separated from each other in the existing mechanism, which may lead to suboptimal solutions and adversely affect the clustering performance. In this article, we propose a novel late fusion multiple kernel clustering with proxy graph refinement (LFMKC-PGR) framework to address these issues. First, we theoretically revisit the connection between late fusion kernel base partition and traditional spectral embedding. Based on this observation, we construct a proxy self-expressive graph from kernel base partitions. The proxy graph in return refines the individual kernel partitions and also captures partition relations in graph structure rather than simple linear transformation. We also provide theoretical connections and considerations between the proposed framework and the multiple kernel subspace clustering. An alternate algorithm with proved convergence is then developed to solve the resultant optimization problem. After that, extensive experiments are conducted on 12 multi-kernel benchmark datasets, and the results demonstrate the effectiveness of our proposed algorithm. The code of the proposed algorithm is publicly available at https://***/wangsiwei2010/graphlatefusion_MKC.

关键词： Kernel Clustering algorithms Optimization partitioning algorithms Task analysis Learning systems Benchmark testing Data fusion multiple kernel clustering (MKC) multi-view learning

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