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Research on Gas Recognition Method Based on Cosine Similarity of Amplitude Ratio Features Under Dynamic Detection

IEEE SENSORS JOURNAL 2025年第8期25卷 13986-13994页

作者： Zhao, Wenjie Hu, Zi'ao Cheng, Haonan Yan, Ruitian Duan, Xiaoyang Yu, Taolin Chen, Yinsheng Xu, Dan Harbin Univ Sci & Technol Higher Educ Key Lab Measuring & Control Technol & Harbin 150080 Heilongjiang Peoples R China Harbin Univ Sci & Technol Sch Measurement & Control Technol & Commun Engn Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Elect & Elect Engn Harbin 150080 Peoples R China Harbin Univ Sci & Technol Key Lab Quantum Manipulat & Control Heilongjiang P Harbin 150080 Peoples R China

Temperature modulation detection methods can significantly enhance the selectivity of semiconductor gas sensors. However, the complexity of similar dynamic response signals in sensor arrays and the optimization of temperature modulation parameters are crucial to the extraction of gas feature information. In this article, the temperature modulation detection method using amplitude-ratio Delta R-a / Delta R-g output is adopted to dynamically test a four-array configuration of commercial metal oxide semiconductor (MOS) gas sensors. Nine kinds of temperature modulation detection modes are established by using the sine-wave modulation waveforms as the excitation signals, constructing a resistance response model based on Delta R-a / Delta R-g , revealing the theoretical basis for the improvement of the selectivity of the temperature modulation. The classification model based on cosine similarity (CS) algorithm was designed to simultaneously realize the qualitative and quantitative detection function of four test gases (CH4, CO, C2H5OH, and H2S). The optimized experimental results indicate that the classification accuracy reaches 100% under modulation mode of sinusoidal 35 V and 45 V bias with a period of 30 s. Compared with the classical algorithms of K-nearest neighbor (KNN), random forest (RF), and radial basis function (RBF) neural network, the simple CS algorithm demonstrates excellent recognition performance, indicating that the amplitude-ratio feature not only controls the scale of data processing but also reduces the dependence on complex pattern recognition algorithms. Under this optimized mode (45 V, 30 s), the concentration prediction results were corrected by linear regression algorithm, and the R-2 was improved from 0.8895 to 0.9762. The good linear output under the amplitude-ratio model contributes to the accurate prediction of gas concentration.

关键词： Sensors Sensor arrays Gas detectors Modulation Temperature sensors Feature extraction Sensor phenomena and characterization Gases Temperature measurement Transient analysis Cosine similarity (CS) feature extraction sine wave temperature modulation

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Parallel Implementation of SPHINCS plus With GPUs

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第6期71卷 2810-2823页

作者： Kim, DongCheon Choi, HoJin Seo, Seog Chung Kookmin Univ Dept Financial Informat Secur Seoul 02707 South Korea

SPHINCS+ was selected as one of NIST Post-quantum Cryptography Digital Signature algorithms (PQC-DSA). However, SPHINCS+ processes are slower compared to other PQC-DSA. When integrating it into protocols (e. g., TLS and IPSec), optimization research from the server perspective becomes crucial. Therefore, we present highly parallel and optimized implementations of SPHINCS+ on various NVIDIA GPU architectures (Pascal, Turing, and Ampere). We discovered parts within the internal processes of SPHINCS+ that could be parallelized and optimized them (e. g., leaf node generation and node merging process in MSS, subtree constructions in FORS, signature generation in WOTS+ and hypertree layer construction), leveraging the characteristics of GPU architecture (e. g., warp-based execution and efficient memory access). As far as we know, this is the first SPHINCS+ implementations on GPUs. Our implementations achieve 44,391(resp. 24,997 and 11,401) signature generations, 725,118(resp. 354,309 and 100,168) key generations, and 285,680(resp. 155,800 and 106,280) verifications per second at security level 1(resp. 3 and 5) on RTX3090. Furthermore, on GTX1070, our SPHINCS+ shows an enhanced throughput of x2.10 for signature generation, x1.03 for key generation, and x9.86 for verification at security level 1, surpassing the study conducted by Sun et al. (IEEE TPDS 2020) on the GTX1080 having 640 more cores than GTX1070.

关键词： Post-quantum cryptography hash-based digital signature algorithm SPHINCS GPU architecture CUDA parallel processing

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Identification of a natural fieldlike entanglement resource in trapped-ion chains

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Physical Review A 2024年第6期109卷 062419-062419页

作者： Natalie Klco D. H. Beck Duke Quantum Center and Department of Physics Department of Physics and Illinois Quantum Information Science and Technology Center

The electromagnetic trapping of ion chains can be regarded as a process of nontrivial entangled quantum state preparation within Hilbert spaces of the local axial motional modes. To begin uncovering properties of this entanglement resource produced as a by-product of conventional ion-trap quantum information processing, the quantum continuous-variable formalism is herein utilized to focus on the leading-order entangled ground state of local motional modes in the presence of a quadratic trapping potential. The decay of entanglement between disjoint subsets of local modes is found to exhibit features of entanglement structure and responses to partial measurement reminiscent of the free massless scalar field vacuum. With significant fidelities between the two, even for large system sizes, a framework is established for initializing quantum field simulations by “imaging” extended entangled states from natural sources, rather than building correlations through deep circuits of few-body entangling operators. By calculating probabilities in discrete Fock subspaces of the local motional modes, we present considerations for locally transferring these predistributed entanglement resources to the qudits of ion internal energy levels, improving this procedure's anticipated experimental viability.

关键词： Entanglement in field theory Measures of continuous variables entanglement quantum algorithms & computation quantum entanglement quantum field theory (low energy) quantum information processing with continuous variables quantum information with trapped ions quantum simulation quantum state engineering Trapped ions Lattice field theory

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quantum Machine Learning in Healthcare: Developments and Challenges

Quantum Machine Learning in Healthcare: Developments and Cha...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Rani, Sita Kumar Pareek, Piyush Kaur, Jaskiran Chauhan, Meetali Bhambri, Pankaj Guru Nanak Dev Engineering College Department of Computer Science and Engineering Ludhiana India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Ipr Cell Bengaluru India Guru Nanak Dev Engineering College Department of Information Technology Ludhiana India

ISBN: (纸本)9798350398465

Machine learning is playing a very significant role to process voluminous data and its classification in a variety of domains. Due to better performance and rapid development in the last decade, quantum computing is also benefiting many areas. With the amalgamation of these two technologies, a new domain for processing big data more efficiently and accurately has evolved, known as quantum machine learning. Healthcare is one of the prominent domains where a huge volume of data is produced from several processes. Efficient processing of healthcare data and records is very important to facilitate many biological and medical processes to provide better treatments to patients. The fundamental aim of this paper is to present a state-of-the-art review of quantum computing concepts, quantum machine learning framework, and the various applications of quantum machine learning in the domain of healthcare. The comparison of QML healthcare models with ML-based healthcare applications is discussed in this work. The authors also present the various challenges faced in the deployment of quantum machine learning algorithms in the domain of healthcare and possible future research directions. © 2023 IEEE.

关键词： Health care

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quantum Representation Based Job Shop Scheduling

Quantum Representation Based Job Shop Scheduling

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Ripon, Kazi Shah Nawaz Singh, Ashay Oslo Metropolitan University Oslo Norway Ostfold University College Halden Norway

ISBN: (纸本)9781665430654

This paper proposes a quantum representation-based genetic algorithm for solving the job-shop scheduling problem, aiming to minimize the makespan. The job-shop scheduling is a typical scheduling problem that falls under the NP-hard combinatorial optimization problems and has undergone extensive investigation in the literature. Over time, various heuristic and intelligent methods have been developed to tackle this challenging problem. Inspired by the promise of quantum computing, this paper explores using quantum information representation and processing techniques to enhance the performance of conventional genetic algorithms on classical computers to solve the job-shop scheduling problem. The proposed quantum-inspired genetic algorithm employs a conversion mechanism of quantum representation to code the schedule;and utilizes a rotation angle table to update the population. The effectiveness of the quantum-inspired genetic algorithm is compared to that of a standard genetic algorithm, with experimental results confirming the potential of the proposed approach in tackling complex combinatorial optimization problems. © 2023 IEEE.

关键词： converted quantum representation job-shop scheduling problem quantum genetic algorithm rotation angle table

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A Parallelized Qubit Mapping Algorithm for Large-scale quantum Circuits

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IEIE Transactions on Smart processing and Computing 2022年第1期11卷 40-48页

作者： Kim, Dongmin Heng, Sengthai Han, Youngsun Department of AI Convergence Pukyong National University Pusan48513 Korea Republic of Department of Computer Engineering Pukyong National University Pusan48513 Korea Republic of

quantum computers are superior to existing classical supercomputers in terms of run time for specific tasks, and various studies on quantum computers are in progress. Despite this, there is a significant gap in technology between available technologies and actual quantum computers on the noisy intermediate-scale quantum-scale. Currently, various quantum algorithms have been implemented and verified using quantum simulations. Therefore, the runtime performance of a quantum circuit can be verified in the same environment as a real quantum machine. A quantum compiler compiles quantum circuits. When a quantum circuit is compiled, logical qubits in a quantum circuit should be mapped to physical qubits, called qubit mapping. As qubit mapping is an NP-Complete problem, the compilation time increases exponentially according to the number of qubits. Therefore, various studies on effective qubit mapping techniques have been developed to accelerate the execution time. This paper proposes a new parallelized Qubit mapping algorithm. The proposed method consists of three-step processes: one circuit is divided into several circuits;the SABRE enhanced algorithm is executed on each circuit in parallel and then merged. The performance was evaluated using 10 benchmarks. The experimental results revealed an acceleration of up to 6.42x and an average speed up of 4.8x, with an average overhead of 1.03x in terms of the number of gates obtained. The proposed method outperformed the well-known modern qubit-mapping algorithm in terms of the execution time with exponential speedup improvement. Copyrights © 2022 The Institute of Electronics and information Engineers.

关键词： Qubits

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Optimal quantum state transformations based on machine learning

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quantum information processing 2021年第6期20卷 1-18页

作者： Zhao, Tian-Hui Wang, Ming-Hao Zhou, Bin Hubei Univ Dept Phys Wuhan 430062 Peoples R China

It is well known that quantum algorithms may solve problems efficiently that are intractable using conventional algorithms. quantum algorithms can be designed with a set of universal quantum gates that transform input states into desired output states. However, designing quantum algorithms that transform states in desired ways is challenging due to its complexity. In this paper, we propose a machine learning framework for the transformation of unknown states into their corresponding target states. Specifically, a parameterized quantum circuit learns a given task by tuning its parameters. After the learning is done, the circuit is competent for the quantum task. This allows us to circumvent cumbersome circuit design based on universal quantum gates. If perfect transformation is forbidden by quantum theory, an optimal transformation can be obtained in terms of fidelity. This provides a research method to study various quantum no-go theorems that characterize the intrinsic gap between quantum and classical information. As examples, quantum state rotation and quantum state cloning are studied using numerical simulations. We also show the good robustness of our machine learning framework to corrupted training data, which is a very nice property for physical implementation on near-term noisy intermediate-scale quantum devices.

关键词： quantum state transformation Machine learning quantum cloning

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Effect of photonic errors on quantum enhanced dense-subgraph finding

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Physical Review Applied 2023年第5期20卷 054043-054043页

作者： Naomi R. Solomons Oliver F. Thomas Dara P. S. McCutcheon Quantum Engineering Centre for Doctoral Training Centre for Nanoscience and Quantum Information University of Bristol Bristol BS8 1FD United Kingdom Quantum Engineering Technology Labs H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering University of Bristol Bristol BS8 1UB United Kingdom Duality Quantum Photonics 6 Lower Park Row Bristol BS1 5BJ United Kingdom

We investigate the effects of photon loss and spectrally impure sources on Gaussian boson sampling (GBS) when used in dense-subgraph-finding algorithms. We find that the effectiveness of these algorithms is remarkably robust to such errors, to such an extent that there exist classical algorithms that can efficiently simulate the underlying GBS. These results suggest that, unlike the GBS problem itself, the speed-up of GBS-based algorithms over classical approaches when applied to the dense-subgraph problem is not exponential. They do suggest, however, that any advantage offered could be achieved on a quantum device with far less stringent requirements on photon loss and purity than general GBS.

关键词： Boson sampling Optical quantum information processing Photonics quantum computation quantum information processing with continuous variables Graph theory Optical computing

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Benchmarking the performance of portfolio optimization with QAOA

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quantum information processing 2022年第1期22卷 1-27页

作者： Brandhofer, Sebastian Braun, Daniel Dehn, Vanessa Hellstern, Gerhard Huels, Matthias Ji, Yanjun Polian, Ilia Bhatia, Amandeep Singh Wellens, Thomas Univ Stuttgart Inst Tech Informat Pfaffenwaldring 47 D-70569 Stuttgart Germany Eberhard Karls Univ Tubingen Inst Theoret Phys Morgenstelle 14 D-72076 Tubingen Germany DHBW Ravensburg Zentrum Digitale Innovationen ZDI Marktstr 28 D-88214 Ravensburg Germany Fraunhofer Inst Angew Festkorperphys IAF Tullastr 72 D-79108 Freiburg Germany

We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization constrained on the number of assets contained in the portfolio. QAOA has been suggested as a possible candidate for solving this problem (and similar combinatorial optimization problems) more efficiently than classical computers in the case of a sufficiently large number of assets. However, the practical implementation of this algorithm requires a careful consideration of several technical issues, not all of which are discussed in the present literature. The present article intends to fill this gap and thereby provides the reader with a useful guide for applying QAOA to the portfolio optimization problem (and similar problems). In particular, we will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters. Viewing at the application of QAOA on error-prone NISQ hardware, we also analyse the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware). Finally, we define a criterion for distinguishing between 'easy' and 'hard' instances of the portfolio optimization problem.

关键词： quantum computing quantum algorithm QAOA quantum optimization

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Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm

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Physical Review A 2024年第3期110卷 032434页

作者： T. Muser E. Zapusek V. Belis F. Reiter Institute for Quantum Electronics Institute for Particle Physics and Astrophysics

There is an ongoing effort to find quantum speedups for learning problems. Recently [Y. Liu et al., Nat. Phys. 17, 1013 (2021)] proved an exponential speedup for quantum support vector machines by leveraging the speedup of Shor's algorithm. We expand upon this result and identify a speedup utilizing Grover's algorithm in the kernel of a support vector machine. To show the practicality of the kernel structure we apply it to a problem related to pattern matching, providing a practical yet provable advantage. Moreover, we show that combining quantum computation in a preprocessing step with classical methods for classification further improves classifier performance.

关键词： Machine learning quantum algorithms quantum algorithms & computation quantum computation quantum information processing

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