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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： BBNS. Prakash B. Naveen MD. Akhiluzzama Pothuraju Rajarajeswari Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

This paper shows the distinction between quantum and Classical Machine Learning techniques appeal to a diabetic mellitus dataset. Diabetes mellitus is like series of diseases that affect the body and deplete blood sugar levels (insulin). In our bodies, glucose is an important source of energy for powering mitochondria, the cells' function that make muscles and tissues strong. We use many machine learning classifiers such as Support Vector Machine, Kernel Principal Component Analysis, Bayesian Network and Decision Tree etc. These models are able predict a certain amount of data, while in case of large data there is significant amount of error and low accuracy rate, A new method known as quantum machine learning (QML) is nothing but the collaboration of machine learning in the way of quantum algorithms the term used for machine learning algorithms executed in a quantum computer for analysis of classical data is known as quantum-enhanced machine learning. In this method we are predicting diabetes mellitus using quantum machine learning algorithms which able to handle the huge data with high accuracy rate of 97% and F1-Score of 68%.Our study having implemented models of Predicted & Enhanced models.

关键词： Support vector machines Machine learning algorithms quantum algorithm Computational modeling Machine learning Predictive models Data models

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An Improved Artificial Potential Field Path Planning Based on Gradient Statistical Mutation quantum Genetic algorithm

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IEEE ACCESS 2024年 12卷 119292-119301页

作者： Yuan, Zhenguo Qin, Huiping Li, Hui Guangdong Ind Polytech Univ Sch Mech & Elect Technol Guangzhou 510000 Peoples R China Harbin Univ Commerce Sch Comp & Informat Engn Harbin 150028 Peoples R China

In the field of vehicle path planning, the traditional Artificial Potential Field (APF) has the disadvantage that it is difficult to jump out of the local extremum. Therefore, an improved Artificial Potential Field based on the Gradient Statistical Mutation quantum Genetic algorithm (GSM-QGA) is proposed. Based on the traditional circular influence domain, the dynamic elliptical influence domain of the repulsive potential field is proposed by analyzing the motion state of the vehicle and obstacles. Through an analysis of the factors influencing the potential field function, the velocity factor is introduced to design the repulsive and attractive potential field functions. The GSM-QGA is introduced as a modified local optimal correction strategy for the improved APF. When the vehicle falls into the local extremum reciprocating motion, a pseudo-global map is constructed based on the current position of the vehicle, and a feasible path to jump out of the local extremum range is planned. The effectiveness of the improved algorithm in vehicle path planning is proved by simulation experiments. Compared with other algorithms, the path length planned by the improved algorithm is shorter and the path is smoother. The algorithm can effectively jump out of the local optimal state.

关键词： Path planning Heuristic algorithms Genetic algorithms Vehicle dynamics Collision avoidance Stability analysis Gradient methods quantum algorithm Artificial potential field gradient statistical mutation quantum genetic algorithm path planning adaptive ellipse influence domain local minimum

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OPTIMIZATION AND NOISE ANALYSIS OF THE quantum algorithm FOR SOLVING ONE-DIMENSIONAL POISSON EQUATION

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quantum INFORMATION & COMPUTATION 2022年第7-8期22卷 569-593页

作者： Cui, Guolong Wang, Zhimin Wang, Shengbin Shi, Shangshang Shang, Ruimin Li, Wendong Wei, Zhiqiang Gu, Yongjian Ocean Univ China Coll Informat Sci & Engn Qingdao 266100 Peoples R China Pilot Natl Lab Marine Sci & Technol Qingdao High Performance Comp Ctr Qingdao 266100 Peoples R China

Solving differential equations is one of the most promising applications of quantum computing. Recently we proposed an efficient quantum algorithm for solving one-dimensional Poisson equation avoiding the need to perform quantum arithmetic or Hamiltonian simulation. In this paper, we further develop this algorithm to make it closer to the real application on the noisy intermediate-scale quantum (NISQ) devices. To this end, we first optimize the quantum ID-Poisson solver by developing a new way of performing the sine transformation. The circuit depth for implementing the sine transform is reduced from n(2) to n without increasing the total qubit cost of the whole circuit, which is achieved by neatly reusing the additional ancillary quits. Then, we analyse the effect of common noise existing in the real quantum devices on our algorithm using the IBM Qiskit toolkit. We find that the phase damping noise has little effect on our algorithm, while the bit flip noise has the greatest impact. In addition, threshold errors of the quantum gates are obtained to make the fidelity of the circuit output being greater than 90%. The results of noise analysis will provide a good guidance for the subsequent work of error correction for our algorithm. The noise-analysis method developed in this work can be used for other algorithms to be executed on the NISQ devices.

关键词： quantum algorithm Poisson equation quantum noise

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quantum algorithm for preparing the ground state of a physical system through multi-step quantum resonant transitions

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quantum INFORMATION PROCESSING 2021年第1期20卷 40-40页

作者： Wang, Hefeng Yu, Sixia Xi An Jiao Tong Univ MOE Key Lab Nonequilibrium Synth & Modulat Conden Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Phys Xian 710049 Peoples R China Shaanxi Prov Key Lab Quantum Informat & Quantum O Xian 710049 Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Dept Modern Phys Hefei 230026 Anhui Peoples R China

We present a quantum algorithm for preparing the ground state of a physical system by using a multi-step quantum resonant transition (QRT) method. We construct a sequence of intermediate Hamiltonians to reach the system Hamiltonian, and then, starting from the ground state of an initial Hamiltonian, the system is sequentially evolved to the ground states of the intermediate Hamiltonians step by step using the QRT method, finally reaching the ground state of the system Hamiltonian. The algorithm can be run efficiently if the overlap between the ground states of any two adjacent Hamiltonians and the energy gap between the ground state and the first excited state of each Hamiltonian are not exponentially small. By using this algorithm, preparing the ground state of a system is transformed to simulating the time evolution of a sequence of time-independent Hamiltonians. This algorithm can also be used for calculating energy eigenvalues of a physical system.

关键词： quantum computation quantum simulation quantum algorithm quantum resonant transition quantum state preparation C63 Computational Techniques Simulation Modeling 03 67 Ac 03 67 Lx

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A Query-Efficient quantum algorithm for Maximum Matching on General Graphs 17th

A Query-Efficient Quantum Algorithm for Maximum Matching on ...

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17th International Symposium on algorithms and Data Structures (WADS)

作者： Kimmel, Shelby Witter, R. Teal Middlebury Coll Dept Comp Sci Middlebury VT 05753 USA NYU Tandon Sch Engn Dept Comp Sci & Engn New York NY 10013 USA

ISBN: (纸本)9783030835088;9783030835071

We design quantum algorithms for maximum matching. Working in the query model, in both adjacency matrix and adjacency list settings, we improve on the best known algorithms for general graphs, matching previously obtained results for bipartite graphs. In particular, for a graph with n vertices and m edges, our algorithm makes O(n(7/4)) queries in the matrix model and O(n(3/4)(m + n)(1/2)) queries in the list model. Our approach combines Gabow's classical maximum matching algorithm [Gabow, Fundamenta Informaticae, '17] with the guessing tree method of Beigi and Taghavi [Beigi and Taghavi, quantum, '20].

关键词： Maximum matching quantum algorithm

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quantum algorithm for quantum state discrimination via partial negation and weak measurement

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quantum INFORMATION PROCESSING 2021年第9期20卷 307-307页

作者： Rizk, Doha A. Younes, Ahmed Alexandria Univ Dept Math & Comp Sci Fac Sci Alexandria Egypt Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

The quantum state discrimination problem is to distinguish between non-orthogonal quantum states. This problem has many applications in quantum information theory, quantum communication and quantum cryptography. In this paper, a quantum algorithm using weak measurement and partial negation will be proposed to solve the quantum state discrimination problem using a single copy of an unknown qubit. The usage of weak measurement makes it possible to reconstruct the qubit after measurement since the superposition will not be destroyed due to measurement. The proposed algorithm will be able to determine, with high probability of success, the state of the unknown qubit and whether it is encoded in the Hadamard or the computational basis by counting the outcome of the successive measurements on an auxiliary qubit.

关键词： quantum state discrimination quantum algorithm Weak measurement Partial negation Computational basis Hadamard basis

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quantum mean centering for block-encoding-based quantum algorithm

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2022年 607卷

作者： Liu, Hai-Ling Yu, Chao-Hua Wan, Lin-Chun Qin, Su-Juan Gao, Fei Wen, Qiaoyan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China State Key Lab Cryptol POB 5159 Beijing 100878 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330032 Jiangxi Peoples R China Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China

Mean Centering (MC) is an important data preprocessing technique, which has a wide range of applications in data mining, machine learning, and multivariate statistical analysis. When the data set is large, this process will be time-consuming. In this paper, we propose an efficient quantum MC algorithm based on the block-encoding technique, which enables the existing quantum algorithms can get rid of the assumption that the original data set has been classically mean-centered. Specifically, we first adopt the strategy that MC can be achieved by multiplying by the centering matrix C, i.e., removing the row means, column means and row-column means of the original data matrix X can be expressed as XC, CX and CXC, respectively. This allows many classical problems involving MC, such as Principal Component Analysis (PCA), to directly solve the matrix algebra problems related to XC, CX or CXC. Next, we can employ the block-encoding technique to realize MC. To achieve it, we first show how to construct the block-encoding of the centering matrix C, and then further obtain the block-encodings of XC, CX and CXC. Finally, we describe one by one how to apply our MC algorithm to PCA and other algorithms. (c) 2022 Published by Elsevier B.V.

关键词： Mean centering quantum algorithm Block-encoding Matrix algebra problems The centering matrix

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quantum algorithm of Imperfect KB Self-organization. Pt II: Robotic Control with Remote Knowledge Base Exchange

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Artificial Intelligence Advances 2021年第2期3卷 44-70页

作者： A.G.Reshetnikov S.V.Ulyanov Plekhanov Russian University of Economics Laboratory of cloud technologies and Big Data analyticsRussia State University Dubna Russia

The technology of knowledge base remote design of the smart fuzzy controllers with the application of the"Soft/quantum computing optimizer"toolkit software *** possibility of the transmission and communication the knowledge base using remote connection to the control object *** and communication of the fuzzy controller’s knowledge bases implemented through the remote connection with the control object in the online mode apply the Bluetooth or WiFi *** transmission of knowledge bases allows designing many different built-in intelligent controllers to implement a variety of control strategies under conditions of uncertainty and *** examples,two different models of robots described(mobile manipulator and(“cart-pole”system)inverted pendulum).A comparison of the control quality between fuzzy controllers and quantum fuzzy controller in various control modes is *** ability to connect and work with a physical model of control object without using than mathematical model *** implemented technology of knowledge base design sharing in a swarm of intelligent robots with quantum *** allows to achieve the goal of control and to gain additional knowledge by creating a new quantum hidden information source based on the synergetic effect of combining ***ment and implementation of intelligent robust controller’s prototype for the intelligent quantum control system of mega-science project NICA(at the first stage for the cooling system of superconducted magnets)is *** results of the experiments demonstrate the possibility of the ensured achievement of the control goal of a group of robots using soft/quantum computing technologies in the design of knowledge bases of smart fuzzy controllers in quantum intelligent control *** developed software toolkit allows to design and setup complex ill-defined and weakly formalized technical systems on line.

关键词： quantum software engineering quantum algorithm Group of intelligent robots Multi-agent system Intelligent control Fuzzy controller Remote transmission of knowledge Knowledge synergetic

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A quantum algorithm of multislice method

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Results in Physics 2024年 62卷

作者： Wang, Y.C. Sun, Y. Ding, Z.J. Department of Physics University of Science and Technology of China Anhui Hefei 230026 China Department of Physics Xiamen University Fujian Xiamen 361005 China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Anhui Hefei 230026 China

Multislice method is widely used to simulate the elastic interaction of an electron beam with matter in electron microscopy. In this paper, we propose a quantum algorithm of multislice method. With the use of quantum circuit this quantum version of multislice method costs less storage space and less runtime than the classical algorithm by taking advantage of the feature of quantum computing. We have used a classical supercomputer to simulate a quantum circuit, and successfully carried out the quantum multislice computation and obtained the expected result on a virtual quantum circuit, which proves the feasibility and correctness of the quantum algorithm. This work demonstrates the potential application prospect of quantum computing to the research of computational physics as a good example. © 2024 The Author(s)

关键词： Electron scattering and diffraction Multislice method quantum algorithm quantum computing quantum simulation

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An exact quantum algorithm for testing Boolean functions with one uncomplemented product of two variables

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quantum INFORMATION PROCESSING 2020年第7期19卷 213-213页

作者： Chen, Chien-Yuan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn 700 Kaohsiung Univ Rd Kaohsiung Taiwan

In this paper, we propose a novel quantum learning algorithm, based on Younes' quantum circuit, to find dependent variables of the Boolean function f: {0,1}(n)->{0,1} with one uncomplemented product of two variables. Typically, in the worst-case scenario, two dependent variables are found by evaluating the function O (n) times. However, our proposed quantum algorithm only requires O (log(2)n) function operations in the worst-case. Additionally, we evaluate the average number to perform the function. In the average case, our algorithm requires O (1) function operations.

关键词： quantum algorithm quantum learning algorithm The Bernstein-Vazirani algorithm

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