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Two End-to-End quantum-Inspired Deep Neural Networks for Text Classification

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第4期35卷 4335-4345页

作者： Shi, Jinjing Li, Zhenhuan Lai, Wei Li, Fangfang Shi, Ronghua Feng, Yanyan Zhang, Shichao Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha 410004 Peoples R China

In linguistics, the uncertainty of context due to polysemy is widespread, which attracts much attention. quantum-inspired complex word embedding based on Hilbert space plays an important role in natural language processing (NLP), which fully leverages the similarity between quantum states and word tokens. A word containing multiple meanings could correspond to a single quantum particle which may exist in several possible states, and a sentence could be analogous to the quantum system where particles interfere with each other. Motivated by quantum-inspired complex word embedding, interpretable complex-valued word embedding (ICWE) is proposed to design two end-to-end quantum-inspired deep neural networks (ICWE-QNN and CICWE-QNN representing convolutional complex-valued neural network based on ICWE) for binary text classification. They have the proven feasibility and effectiveness in the application of NLP and can solve the problem of text information loss in CE-Mix [1] model caused by neglecting the important linguistic features of text, since linguistic feature extraction is presented in our model with deep learning algorithms, in which gated recurrent unit (GRU) extracts the sequence information of sentences, attention mechanism makes the model focus on important words in sentences and convolutional layer captures the local features of projected matrix. The model ICWE-QNN can avoid random combination of word tokens and CICWE-QNN fully considers textual features of the projected matrix. Experiments conducted on five benchmarking classification datasets demonstrate our proposed models have higher accuracy than the compared traditional models including CaptionRep BOW, DictRep BOW and Paragram-Phrase, and they also have great performance on F1-score. Eespecially, CICWE-QNN model has higher accuracy than the quantum-inspired model CE-Mix as well for four datasets including SST, SUBJ, CR and MPQA. It is a meaningful and effictive exploration to design quantum-inspired dee

关键词： quantum mechanics Feature extraction Deep learning Task analysis Natural language processing Linguistics Springs Complex-valued word embedding text classification deep neural network deep learning natural language processing

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HyperQUEEN-MF: Hyperspectral quantum Deep Network with Multi-Scale Feature Fusion For quantum Image Super-Resolution 13

HyperQUEEN-MF: Hyperspectral Quantum Deep Network with Multi...

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13rd IEEE Sensor Array and Multichannel Signal processing Workshop (SAM)

作者： Hsu, Shih-Min Lin, Tzu-Hsuan Lin, Chia-Hsiang Natl Cheng Kung Univ Dept Elect Engn Inst Comp & Commun Engn Tainan Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan Natl Cheng Kung Univ Miin Wu Sch Comp Tainan Taiwan

ISBN: (纸本)9798350344820;9798350344813

Hyperspectral images (HSIs) have drawn considerable attention over the last decade thanks to their high spectral resolution. However, as hardware limitations often cause the low spatial resolution of HSIs compared to traditional RGB and multispectral images, the hyperspectral image super-resolution (HSR) problem becomes crucial in facilitating the subsequent material identification applications. Recently, we developed hyperspectral quantum deep network (HyperQUEEN) for satellite data restoration, thereby proving that quantum features do play some role in developing high-performance inverse imaging algorithms. Before HyperQUEEN, which elegantly performs the hyperspectral matrix/tensor factorization via the so-called low-rank module, other existing quantum image processing tasks can only achieve basic geometry transforms or simple classification-level tasks. In this work, we go a step further by investigating the quantum implementation of multi-scale feature fusion (MF) transformer and applying the HyperQUEEN-MF to solve the challenging inverse problem known as single HSR (SHSR). By learning multi-scale quantum features, the proposed HyperQUEEN-MF does show outstanding SHSR performances, while the experimental results also show the feasibility of quantum-driven hyperspectral super-resolution. This creates a door for solving other advanced challenging inverse problems via quantum feature information extraction and processing.

关键词： quantum neural network hyperspectral image hyperspectral super-resolution quantum deep learning quantum image processing

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quantum Computing and Cybersecurity: Threat or Opportunity? 23rd

Quantum Computing and Cybersecurity: Threat or Opportunity?

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23rd International Conference on Informatics in Economy, IE 2024

作者： Butoi, Alexandru Business Information Systems Department Babeş-Bolyai University Cluj-Napoca Romania

ISBN: (纸本)9789819601608

quantum computing perspective is that it will revolutionize the way we implement and execute highly intensive processing algorithms or large space solution search heuristics. So algorithms of polynomial or exponential complexity will deliver their results in matters of hours, rather than days and the implications for classic and de-facto security algorithms are disruptive. We explore some possibilities for leveraging quantum computing power to secure data stored and exchanged in non-quantum environments while there is enough evidence in the existing literature that quantum algorithms may have the potential to level up the security level of a hybrid computational ecosystem consisting of a few quantum nodes and several non-quantum nodes. We explore a possible way of how we can use quantum algorithms to secure data stored in non-quantum environments in front of quantum threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Qubits

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Factor Graphs for quantum information processing

arXiv

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arXiv 2022年

作者： Cao, Michael Xuan The Chinese University of Hong Kong Hong Kong

Statistical graphical models are frameworks that use graphs to represent dependencies among a large number of random variables. Such frameworks have been proven useful for developing analytical/algorithmic methods in characterizing the behavior of systems involving a large number of variables. Among these frameworks, the factor graphs are a popular variant. A factor graph represents a factorization of a multivariate function, and in many practical examples, a factorization of some probability function. It is a recurring problem in physics and engineering to compute the marginals (or partition sums) of a given multivariate function. With the factor graph representation, such marginals (or partition sums) can be computed/estimated via a class of algorithms known as belief-propagation algorithms. In recent years, quantum computing and quantum communications have received increasing attention. In these applications, information is represented and processed by suitable quantum systems. quantum theory, which is a generalization of probability theory, describes the uncertainty of quantum systems. Classical graphical models, which are based on probability theory, can no longer efficiently describe the dependencies among such systems. In this thesis, we are interested in generalizing factor graphs and the relevant methods toward describing quantum systems. Two generalizations of classical graphical models are investigated, namely double-edge factor graphs (DeFGs) and quantum factor graphs (QFGs). Conventionally, a factor in a factor graph represents a nonnegative real-valued local functions. Two different approaches to generalize factors in classical factor graphs yield DeFGs and QFGs, respectively. We proposed/re-proposed and analyzed generalized versions of belief-propagation algorithms for DeFGs/QFGs. As a particular application of the DeFGs, we investigate the information rate and their upper/lower bounds of classical communications over quantum channels with memory. In

关键词： quantum optics

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QSurfNet: a hybrid quantum convolutional neural network for surface defect recognition

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quantum information processing 2023年第5期22卷 1-28页

作者： Mishra, Shraddha Tsai, Chi-Yi Tamkang Univ Dept Elect & Comp Engn 151 Yingzhuan Rd New Taipei 251 Taiwan

In this paper, we propose a novel hybrid quantum-classical convolutional neural network named QSurfNet, inspired by an efficient surface defect recognition model called SurfNetv2. SurfNetv2 is an established high-speed classical convolution neural network (CNN) model for image recognition, and QSurfNet further inherits the legacy by introducing quantum CNN (QCNN) layers, reducing the number of convolution blocks in the model architecture and the image size required for recognition. The QSurfNet architecture consists of a QCNN module, a feature extraction module, and a surface defect recognition module. The algorithm works on end-to-end supervised quantum machine learning and deep learning techniques to classify the surface defect categories of the surface defect image datasets. For this research, we used the 8 x 8-pixel and 12 x 12-pixel resolution RGB image information from the public Northeastern University dataset, and an industry-sourced calcium silicate board private dataset. We used principal component analysis for image dimensionality reduction across the R, G, and B channels, individually. We compare the performance of QSurfNet with six state-of-the-art methods on these datasets upon recognition results on test accuracy, recall, precision, and F1-Measure performance metrics. QSurfNet is novel in terms of the algorithm design methodology that can turn any classical CNN algorithm into state-of-the-art QCNN. Hence, the proposed methodology contributes to the practical feasibility of developing novel convolutional architecture designs of hybrid quantum-classical algorithms.

关键词： quantum convolutional neural network quantum machine learning Surface defect recognition Parameterized quantum circuit Industry 4 0

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Optimal training of finitely sampled quantum reservoir computers for forecasting of chaotic dynamics

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quantum MACHINE INTELLIGENCE 2025年第1期7卷 1-16页

作者： Ahmed, Osama Tennie, Felix Magri, Luca Imperial Coll London Dept Aeronaut Exhibit Rd London SW7 2BX England Univ London Dept Engn City St GeorgesNorthampton Sq London EC1V 0HB England Alan Turing Inst London NW1 2DB England Politecn Torino DIMEAS Corso Duca Abruzzi24 I-10129 Turin Italy

In the current Noisy Intermediate Scale quantum (NISQ) era, the presence of noise deteriorates the performance of quantum computing algorithms. quantum reservoir computing (QRC) is a type of quantum machine learning algorithm, which, however, can benefit from different types of tuned noise. In this paper, we analyze how finite sampling noise affects the chaotic time series prediction of the gate-based QRC and recurrence-free quantum reservoir computing (RF-QRC) models. First, we examine RF-QRC and show that, even without a recurrent loop, it contains temporal information about previous reservoir states using leaky integrated neurons. This makes RF-QRC different from quantum extreme learning machines (QELM). Second, we show that finite sampling noise degrades the prediction capabilities of both QRC and RF-QRC while affecting QRC more due to the propagation of noise. Third, we optimize the training of the finite-sampled quantum reservoir computing framework using two methods: (a) singular value decomposition (SVD) applied to the data matrix containing noisy reservoir activation states and (b) data-filtering techniques to remove the high frequencies from the noisy reservoir activation states. We show that denoising reservoir activation states improves the signal-to-noise ratios with smaller training loss. Finally, we demonstrate that the training and denoising of the noisy reservoir activation signals in RF-QRC are highly parallelizable on multiple quantum processing units (QPUs) as compared to the QRC architecture with recurrent connections. The analyses are numerically showcased on prototypical chaotic dynamical systems with relevance to turbulence. This work opens opportunities for using quantum reservoir computing with finite samples for time series forecasting on near-term quantum hardware.

关键词： quantum reservoir computing Sampling noise Recurrence-free quantum reservoir computing Chaos Turbulence

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Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms

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quantum information processing 2025年第2期24卷 1-22页

作者： Ponce, Moises Herrman, Rebekah Lotshaw, Phillip C. Powers, Sarah Siopsis, George Humble, Travis Ostrowski, James Univ Tennessee Knoxville Dept Ind & Syst Engn Knoxville TN 37996 USA Oak Ridge Natl Lab Quantum Computat Sci Grp Oak Ridge TN 37830 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37830 USA Univ Tennessee Knoxville Dept Phys & Astron Knoxville TN 37996 USA Oak Ridge Natl Lab Quantum Sci Ctr Oak Ridge TN 37830 USA

The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current noisy quantum devices cannot solve large problems due to hardware constraints. In this work, we develop an algorithm that decomposes the QAOA input problem graph into a smaller problem and solves MaxCut using QAOA on the reduced graph. The algorithm requires a subroutine that can be classical or quantum-in this work, we implement the algorithm twice on each graph. One implementation uses the classical solver Gurobi in the subroutine and the other uses QAOA. We solve these reduced problems with QAOA. On average, the reduced problems require only approximately 1/10 of the number of vertices than the original MaxCut instances. Furthermore, the average approximation ratio of the original MaxCut problems is 0.75, while the approximation ratios of the decomposed graphs are on average of 0.96 for both Gurobi and QAOA. With this decomposition, we are able to measure optimal solutions for ten 100-vertex graphs by running single-layer QAOA circuits on the Quantinuum trapped-ion quantum computer H1-1, sampling each circuit only 500 times. This approach is best suited for sparse, particularly k-regular graphs, as k-regular graphs on n vertices can be decomposed into a graph with at most nkk+1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{nk}{k+1}$$\end{document} vertices in polynomial time. Further reductions can be obtained with a potential trade-off in computational time. While this paper applies the decomposition method to the MaxCut problem, it can be applied to more general classes of combinatorial optimization problems.

关键词： MaxCut QAOA Combinatorial optimization

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Unleashing quantum Neural Networks: Solving Parameterized quantum Circuit Challenges

Unleashing Quantum Neural Networks: Solving Parameterized Qu...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Prakash, Kolla Bhanu A.P. Vaddeswaram India

ISBN: (纸本)9798350371567

A possible approach to tackling computationally demanding issues beyond the capabilities of conventional algorithms is the use of quantum circuits, which provide a fundamental foundation for utilizing the power of quantum computation. This abstract examines how quantum circuits are implemented, emphasizing important topics such as circuit optimization, gate operations, and qubit manipulation. We explore different methods of encoding quantum information into topological qubits, trapped ions, and superconducting circuits, among other physical qubit formats. Furthermore, we go over the function of gate operations in quantum circuits, emphasizing crucial gates like phase, Hadamard, and CNOT gates, which serve as the foundation for quantum algorithms. Additionally, we look at methods for maximizing the efficiency and dependability of quantum processing by designing quantum circuits to minimize gate counts, depth, and error rates. To enhance the capabilities of quantum computing, this abstract presents a novel synthesis of quantum neural networks (QNNs) and quantum parameterized circuits. Through the use of parameterized quantum circuits' inherent flexibility, our approach makes it easier to design and optimize quantum circuits with flexible parameters, providing a flexible foundation for a variety of quantum algorithms. Using this amalgamation, we unleash the capacity of quantum computers to address intricate optimization and machine learning assignments with unparalleled effectiveness and expandability. Our solution bridges the gap between traditional machine learning approaches and quantum computing paradigms by enabling the smooth integration of classical data processing techniques with quantum computation. We use quantum states as data representations in our implementation of quantum neural networks, and we optimize trainable parameters to meet predetermined goals. This new paradigm opens up the investigation of the inherent features of quantum states and provides ne

关键词： Timing circuits

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Two-qubit entangling gates for superconducting quantum computers

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RESULTS IN PHYSICS 2024年 56卷

作者： Abughanem, Muhammad Eleuch, Hichem Ain Shams Univ Fac Sci Cairo 11566 Egypt Zewail City Sci Technol & Innovat Giza 12678 Egypt Univ Sharjah Dept Appl Phys & Astron Sharjah U Arab Emirates Abu Dhabi Univ Coll Arts & Sci Abu Dhabi U Arab Emirates Texas A&M Univ Inst Quantum Sci & Engn College Stn TX 77843 USA

Noisy intermediate-scale quantum computers (NISQ) have demonstrated superior performance compared to conventional computing schemes. Nonetheless, errors associated with two-qubit entangling gates remain a substantial obstacle in fully harnessing the potential of current superconducting quantum computers. The achievement of quantum computation and quantum information processing critically depends on the presence of entangled states. These states are indispensable for quantum computers to surpass classical computers in terms of computational power. This paper presents a comprehensive characterization of a two-qubit entangling gate, known as Dagwood Bumstead (DB) gate, to investigate the challenges posed by errors in the DB gate on NISQ devices. Firstly, we propose a scheme for the realization of the DB gate using two capacitively coupled Josephson Superconducting qubits, based on the experimental setup carried out in Bialczak et al. (2010). Additionally, we introduce and characterize a novel two-qubit quantum gate. Our proposed gate showcases an impressive average gate fidelity of 0.85450. The DB gate's performance is fully described through quantum process tomography (QPT) and quantum state tomography (QST) experiments conducted in three different environments: noisy, noiseless, and on a real IBM quantum computer. The process fidelity of the DB gate is measured to be 0.8069705, and the state fidelity is determined to be 0.8858750. This study offers valuable insights into the performance of the DB gate on a NISQ device, showcasing its potential for executing intricate algorithms and operations on an authentic quantum computer.

关键词： Entangling gates Dagwood Bumstead gate quantum state tomography quantum process tomography Novel two-qubit quantum gates IBM's NISQ quantum computers

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Multiparallel MMT: Faster ISD Algorithm Solving High-Dimensional Syndrome Decoding Problem

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2023年第3期E106A卷 241-252页

作者： Narisada, Shintaro Fukushima, Kazuhide Kiyomoto, Shinsaku KDDI Res Inc Fujimino 3568502 Japan

The hardness of the syndrome decoding problem (SDP) is the primary evidence for the security of code-based cryptosystems, which are one of the finalists in a project to standardize post-quantum cryptography conducted by the U.S. National Institute of Standards and Technology (NIST-PQC). information set decoding (ISD) is a general term for algorithms that solve SDP efficiently. In this paper, we conducted a concrete analysis of the time complexity of the latest ISD algorithms under the limitation of memory using the syndrome decoding estimator proposed by Esser et al. As a result, we present that theoretically nonoptimal ISDs, such as May-Meurer-Thomae (MMT) and May-Ozerov, have lower time complexity than other ISDs in some actual SDP instances. Based on these facts, we further studied the possibility of multiple parallelization for these ISDs and proposed the first GPU algorithm for MMT, the multiparallel MMT algorithm. In the experiments, we show that the multiparallel MMT algorithm is faster than existing ISD algorithms. In addition, we report the first successful attempts to solve the 510-, 530-, 540-and 550-dimensional SDP instances in the Decoding Challenge contest using the multiparallel MMT.

关键词： syndrome decoding problem code-based cryptography information set decoding (ISD) graphics processing unit (GPU)

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