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Learning the Precise Feature for Cluster Assignment

IEEE TRANSACTIONS ON CYBERNETICS 2022年第8期52卷 8587-8600页

作者： Gan, Yanhai Dong, Xinghui Zhou, Huiyu Gao, Feng Dong, Junyu Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Peoples R China Univ Leicester Sch Informat Leicester LE1 7RH Leics England

Clustering is one of the fundamental tasks in computer vision and pattern recognition. Recently, deep clustering methods (algorithms based on deep learning) have attracted wide attention with their impressive performance. Most of these algorithms combine deep unsupervised representation learning and standard clustering together. However, the separation of representation learning and clustering will lead to suboptimal solutions because the two-stage strategy prevents representation learning from adapting to subsequent tasks (e.g., clustering according to specific cues). To overcome this issue, efforts have been made in the dynamic adaption of representation and cluster assignment, whereas current state-of-the-art methods suffer from heuristically constructed objectives with the representation and cluster assignment alternatively optimized. To further standardize the clustering problem, we audaciously formulate the objective of clustering as finding a precise feature as the cue for cluster assignment. Based on this, we propose a general-purpose deep clustering framework, which radically integrates representation learning and clustering into a single pipeline for the first time. The proposed framework exploits the powerful ability of recently developed generative models for learning intrinsic features, and imposes an entropy minimization on the distribution of the cluster assignment by a dedicated variational algorithm. The experimental results show that the performance of the proposed method is superior, or at least comparable to, the state-of-the-art methods on the handwritten digit recognition, fashion recognition, face recognition, and object recognition benchmark datasets.

关键词： Task analysis Clustering algorithms Standards Entropy Training Minimization Optimization Deep clustering entropy minimization generative models representation learning variational algorithm

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Redesign of the Last Mile Delivery Network Using Quantum Alternating Operator Ansatz 16

Redesign of the Last Mile Delivery Network Using Quantum Alt...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Malviya, Gajendra Agarwal, Priyank Sinha, Sudhir Kumar Tcs Research Mumbai India Iit Bombay Mumbai India

ISBN: (纸本)9798350383119

This paper presents an approach for solving the last mile delivery network redesign problem on universal gate-based quantum computing architectures. We formulate the problem as a quantum alternating operator ansatz, a generalization of the quantum approximate optimization algorithm. We define mixers, by using and extending existing families of mixing operators, to constrain search to a feasible subspace depending on the hard constraints of the studied problem. Our study examines the effectiveness of utilizing this approach on multiple problem instances of different sizes. © 2024 IEEE.

关键词： Logistics network redesign QAOA Quantum computing variational algorithm

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Robust Detection of Link Communities With Summary Description in Social Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第6期33卷 2737-2749页

作者： Jin, Di Wang, Xiaobao He, Dongxiao Dang, Jianwu Zhang, Weixiong Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Japan Adv Inst Sci & Technol Sch Informat Sci Nomi Ishikawa 9231211 Japan Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

Community detection has been extensively studied for various applications. Recent research has started to explore node contents to identify semantically meaningful communities. However, links in real networks typically have semantic descriptions and communities of links can better characterize community behaviors than communities of nodes. The second issue in community finding is that the most existing methods assume network topologies and descriptive contents carry the same or compatible information of node group membership, restricting them to one topic per community, which is generally violated in real networks. The third issue is that the existing methods use top ranked words or phrases to label topics when interpreting communities, which is often inadequate for comprehension. To address these issues altogether, we propose a new Bayesian probabilistic approach for modeling real networks and developing an efficient variational algorithm for model inference. Our new method explores the intrinsic correlation between communities and topics to discover link communities and extract semantically meaningful community summaries at the same time. If desired, it is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach and evaluate the method by a case study.

关键词： Network topology Social network services Heuristic algorithms Inference algorithms Clustering algorithms Bayes methods Probabilistic logic Social networks community detection topical summary link communities variational algorithm

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A stochastic evaluation of quantum Fisher information matrix with generic Hamiltonians

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EPJ QUANTUM TECHNOLOGY 2023年第1期10卷 37页

作者： Ho, Le Bin Tohoku Univ Frontier Res Inst Interdisciplinary Sci Sendai Japan Tohoku Univ Grad Sch Engn Dept Appl Phys Sendai 9808579 Japan

Quantum Fisher information matrix (QFIM) is a fundamental quantity in quantum physics, which closely links to diverse fields such as quantum metrology, phase transitions, entanglement witness, and quantum speed limit. It is crucial in quantum parameter estimation, central to the ultimate Cramer-Rao bound. Recently, the evaluation of QFIM using quantum circuit algorithms has been proposed for systems with multiplicative parameters Hamiltonian. However, systems with generic Hamiltonians still lack these proposed schemes. This work introduces a quantum-circuit-based approach for evaluating QFIM with generic Hamiltonians. We present a time-dependent stochastic parameter-shift rule for the derivatives of evolved quantum states, whereby the QFIM can be obtained. The scheme can be executed in universal quantum computers under the family of parameterized gates. In magnetic field estimations, we demonstrate the consistency between the results obtained from the stochastic parameter-shift rule and the exact results, while the results obtained from a standard parameter-shift rule slightly deviate from the exact ones. Our work sheds new light on studying QFIM with generic Hamiltonians using quantum circuit algorithms.

关键词： Quantum metrology Generic Hamiltonian Fisher information Stochastic parameter-shift rule variational algorithm Quantum circuit

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Equivalence of quantum barren plateaus to cost concentration and narrow gorges

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QUANTUM SCIENCE AND TECHNOLOGY 2022年第4期7卷 045015-045015页

作者： Arrasmith, Andrew Holmes, Zoe Cerezo, M. Coles, Patrick J. Los Alamos Natl Lab Theoret Div MS B213 Los Alamos NM 87545 USA Los Alamos Natl Lab Informat Sci Los Alamos NM 87544 USA Los Alamos Natl Lab Ctr Nonlinear Studies Los Alamos NM USA

Optimizing parameterized quantum circuits (PQCs) is the leading approach to make use of near-term quantum computers. However, very little is known about the cost function landscape for PQCs, which hinders progress towards quantum-aware optimizers. In this work, we investigate the connection between three different landscape features that have been observed for PQCs: (1) exponentially vanishing gradients (called barren plateaus (BPs)), (2) exponential cost concentration about the mean, and (3) the exponential narrowness of minima (called narrow gorges). We analytically prove that these three phenomena occur together, i.e., when one occurs then so do the other two. A key implication of this result is that one can numerically diagnose BPs via cost differences rather than via the computationally more expensive gradients. More broadly, our work shows that quantum mechanics rules out certain cost landscapes (which otherwise would be mathematically possible), and hence our results could be interesting from a quantum foundations perspective.

关键词： barren plateau quantum computing quantum machine learning narrow gorge variational algorithm

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Calculating the Green's function of two-site fermionic Hubbard model in a photonic system

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NEW JOURNAL OF PHYSICS 2022年第4期24卷 043030页

作者： Zhu, Jie Nakagawa, Yuya O. Zhang, Yong-Sheng Li, Chuan-Feng Guo, Guang-Can Univ Sci & Technol China Key Lab Quantum Informat CAS Hefei 230026 Peoples R China CAS Ctr Excellence Quantum Informat & Quantum Phy Hefei 230026 Peoples R China QunaSys Inc Bunkyo Ku Aqua Hakusan Bldg 9F1-13-7 Hakusan Tokyo 1130001 Japan

The Green's function has been an indispensable tool to study many-body systems that remain one of the biggest challenges in modern quantum physics for decades. The complicated calculation of the Green's function impedes the research of many-body systems. The appearance of the noisy intermediate-scale quantum devices and quantum-classical hybrid algorithm inspire a new method to calculate the Green's function. Here we design a programmable quantum circuit for photons with utilizing the polarization and the path degrees of freedom to construct a highly-precise variational quantum state of a photon, and first report the experimental realization for calculating the Green's function of the two-site fermionic Hubbard model, a prototypical model for strongly-correlated materials, in photonic systems. We run the variational quantum eigensolver to obtain the ground state and excited states of the model, and then evaluate the transition amplitudes among the eigenstates. The experimental results present the spectral function of the Green's function, which agrees well with exact results. Our demonstration provides the further possibility of the photonic system in quantum simulation and applications in solving complicated problems in many-body systems, biological science and so on.

关键词： the Green's function variational algorithm photonic system

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Supervised learning with a quantum classifier using multi-level systems

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QUANTUM INFORMATION PROCESSING 2020年第3期19卷 1-12页

作者： Adhikary, Soumik Dangwal, Siddharth Bhowmik, Debanjan Indian Inst Technol Delhi Dept Phys New Delhi 110016 India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

We propose a quantum classifier, which can classify data under the supervised learning scheme using a quantum feature space. The input feature vectors are encoded in a single quNit (a N-level quantum system), as opposed to more commonly used entangled multi-qubit systems. For training, we use the much used quantum variational algorithm-a hybrid quantum-classical algorithm, in which the forward part of the computation is performed on a quantum hardware, whereas the feedback part is carried out on a classical computer. We introduce "single-shot training", where all input samples belonging to the same class are used to train the classifier simultaneously. This significantly speeds up the training procedure and provides an advantage over classical machine learning classifiers. We demonstrate successful classification of popular benchmark datasets with our quantum classifier and compare its performance with respect to classical machine learning classifiers. We also show that the number of training parameters in our classifier is significantly less than the classical classifiers.

关键词： Quantum machine learning Quantum neural networks variational algorithm

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Multistate transition dynamics by strong time-dependent perturbation in NISQ era

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JOURNAL OF PHYSICS COMMUNICATIONS 2023年第7期7卷 075004页

作者： Wang, Yulun Krstic, Predrag S. SUNY Stony Brook Inst Adv Computat Sci Stony Brook NY 11794 USA

We develop a quantum computing scheme utilizing McLachlan variational principle in a hybrid quantum-classical algorithm to accurately calculate the transition dynamics of a closed quantum system with many excited states subject to a strong time-dependent perturbation. A systematic approach for optimal construction of a general N-state ansatz with unaryN-qubit encoding is refined. We also use qubit efficient encoding in McLachlan variational quantum algorithm to reduce the number of qubits to log(2) N, simultaneously diminishing depths of the quantum circuits. The significant reduction of the number of time steps is achieved by use of the second order marching method. Instrumental in obtaining high accuracy are adaptations of the circuits to include time-dependent global phase correction. We illustrated, tested and optimized our quantum computing algorithm on a set of 16 bound hydrogenic eigenstates exposed to a strong laser attosecond pulse. Results for transition probabilities are obtained with accuracy better than 1%, as established by comparison to the benchmark data. Use of interaction representation of the Hamiltonian reduces the noise accumulation while the quantum system evolves intime.

关键词： quantum algorithms dynamic simulation variational algorithm

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Mixed Membership Stochastic Blockmodels for Heterogeneous Networks

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BAYESIAN ANALYSIS 2020年第3期15卷 711-736页

作者： Huang, Weihong Liu, Yan Chen, Yuguo Facebook Menlo Pk CA 94025 USA Univ Illinois Dept Stat Champaign IL 61820 USA

Heterogeneous networks are useful for modeling complex systems that consist of different types of objects. However, there are limited statistical models to deal with heterogeneous networks. In this paper, we propose a statistical model for community detection in heterogeneous networks. We formulate a heterogeneous version of the mixed membership stochastic blockmodel to accommodate heterogeneity in the data and the content dependent property of the pairwise relationship. We also apply a variational algorithm for posterior inference. The proposed procedure is shown to be consistent for community detection under mixed membership stochastic blockmodels for heterogeneous networks. We demonstrate the advantage of the proposed method in modeling overlapping communities and multiple memberships through simulation studies and applications to a real data set.

关键词： clustering community detection heterogeneous network mixed membership model stochastic blockmodel variational algorithm

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COLOR AND ANGULAR RECONSTRUCTION OF LIGHT FIELDS FROM INCOMPLETE-COLOR CODED PROJECTIONS

COLOR AND ANGULAR RECONSTRUCTION OF LIGHT FIELDS FROM INCOMP...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hoai-Nam Nguyen Guillemot, Christine Inria Rennes Bretagne Atlantique F-35042 Rennes France

ISBN: (纸本)9781509066315

We present a simple variational approach for reconstructing color light fields (LFs) in the compressed sensing (CS) framework with very low sampling ratio, using both coded masks and color filter arrays (CFAs). A coded mask is placed in front of the camera sensor to optically modulate incoming rays, while a CFA is assumed to be implemented at the sensor level to compress color information. Hence, the LF coded projections, operated by a combination of the coded mask and the CFA, measure incomplete color samples with a three-times-lower sampling ratio than reference methods that assume full color (channel-by-channel) acquisition. We then derive adaptive algorithms to directly reconstruct the light field from raw sensor measurements by minimizing a convex energy composed of two terms. The first one is the data fidelity term which takes into account the use of CFAs in the imaging model, and the second one is a regularization term which favors the sparse representation of light fields in a specific transform domain. Experimental results show that the proposed approach produces a better reconstruction both in terms of visual quality and quantitative performance when compared to reference reconstruction methods that implicitly assume prior color interpolation of coded projections.

关键词： light field compressed sensing demosaicing variational algorithm

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