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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
1
作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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Auxiliary-qubit-free quantum approximate optimization algorithm for the minimum dominating set problem 认领 引用
2
作者 Guanghui Li Xiaohui Ni +5 位作者 Junjian Su Sujuan Qin Fenzhuo Guo Bingjie Xu Wei Huang Fei Gao 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期326-344,共19页
Quantum approximate optimization algorithm(QAOA)is a promising framework for solving combinatorial optimization problems on near-term quantum devices.One such problem is the minimum dominating set(MDS),which is known ... Quantum approximate optimization algorithm(QAOA)is a promising framework for solving combinatorial optimization problems on near-term quantum devices.One such problem is the minimum dominating set(MDS),which is known to be NP-hard.Existing QAOA algorithms for this problem typically require numerous auxiliary qubits,increasing circuit overhead and hardware requirements.In this paper,we propose an auxiliary-qubit-free QAOA algorithm based on Hamiltonian evolution(AQFH-QAOA)for the MDS problem.Unlike previous studies that require numerous auxiliary qubits,our algorithm eliminates the need for auxiliary qubits,thereby significantly reducing circuit overhead.In addition,we present an auxiliary-qubit-free optimized implementation of the previously proposed Guerrero's QAOA algorithm(AQFG-QAOA)by utilizing gate decomposition techniques.Through a detailed analysis of gate complexity,we evaluate the applicability of these two algorithms.Numerical experiments demonstrate that our proposed algorithm achieves competitive solution quality compared with existing QAOA algorithms,making it a promising candidate for implementation on near-term quantum devices. 展开更多
关键词 quantum algorithm quantum approximate optimization algorithm minimum dominating set Boolean algebra identities
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Enhanced multiscale quantum approximate optimization algorithm in multibody combinatorial optimization problems 认领 引用
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作者 Lei-Lei Chen Ping Zou Ya-Fei Yu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期424-434,共11页
At present,the quantum approximate optimization algorithm(QAOA)faces scalability challenges in high-dimensional combinatorial optimization problems due to exponentially growing computational costs and reachability def... At present,the quantum approximate optimization algorithm(QAOA)faces scalability challenges in high-dimensional combinatorial optimization problems due to exponentially growing computational costs and reachability deficits for noisy intermediate-scale quantum(NISQ)devices.This study focuses on the multiscale quantum approximate optimization algorithm(MQAOA),which integrates renormalization group(RG)transformations with QAOA to address these limitations.Based on the connections between the variables in the problem to be solved,the weighted maximal matching method is employed to generate a variable partitioning strategy guiding the RG transformation.This approach not only extends the applicability of MQAOA to satisfiability(SAT)problems—including those with three-body and higher-order interactions in the problem Hamiltonian—but also eliminates the algorithm's sensitivity to problem density.Validations conducted on quantum simulators show that,after running two-round MQAOA,its capability is enhanced to identify optimal solutions with approximately 97%success probability as defined by the ground-state overlap for Max-2-SAT problems(78%success probability for Max-3-SAT problems).The results confirm the feasibility of MQAOA and establish it as a resource-efficient framework for complex combinatorial optimization problems,providing a pathway for NISQ-era deployment. 展开更多
关键词 quantum algorithm parameterized quantum circuit renormalization group transformation combinatorial optimization Max-SAT problems
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Three-party semi-quantum dialogue enhanced with Grover's algorithm based encoding and hypergraph access control 认领 引用
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作者 Rui Tao Jin-Zhe Jiang Zhi-Hua Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期435-447,共13页
We introduce a new three-party semi-quantum dialogue(3P-SQD)protocol that combines GHZ-state-based semiquantum communication,a Grover's algorithm-driven 2-bit encoding scheme,and hypergraph-based access control.In... We introduce a new three-party semi-quantum dialogue(3P-SQD)protocol that combines GHZ-state-based semiquantum communication,a Grover's algorithm-driven 2-bit encoding scheme,and hypergraph-based access control.In each round,the fully quantum participant Alice sends two bits,whereas the semi-quantum participants Bob and Charlie,restricted to semi-quantum operations such as measurements in the computational basis and reflection,each transmit one bit.The protocol incorporates probe state checking,Grover's algorithm-based encoding,and hypergraph-based authorization.It achieves information-theoretic security and controlled access,while preserving high message throughput and imposing no additional requirements on the semi-quantum users. 展开更多
关键词 semi-quantum dialogue Grover’s algorithm based encoding hypergraph access structure quantum communication
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Editorial:Quantum algorithms in computational mechanics 认领 引用
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《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期1-3,共3页
Computational mechanics,as a cornerstone of modern engineering and scientific research,has driven transforma-tive advances across aerospace,energy,biomedical,and other related fields over the past decades.However,the ... Computational mechanics,as a cornerstone of modern engineering and scientific research,has driven transforma-tive advances across aerospace,energy,biomedical,and other related fields over the past decades.However,the ever-increasing demand for high-fidelity simulations of complex systems has pushed classical computing archi-tectures to their performance limits.The inherent ex-ponential complexity of multiscale,multiphysics problems often leads to prohibitive computational costs,creating a bottleneck for next-generation engineering innovation. 展开更多
关键词 multiscale problems complex systems classical computing computational mechanicsas engineering innovation scientific researchhas computational mechanics quantum algorithms
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Quantum computing-enhanced topology optimization with stress constraints for truss structures 认领 引用 被引量：2
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作者 Yan Wang Dixiong Yang +1 位作者 Zhenzeng Lei Guohai Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期41-57,共17页
Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly e... Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly enhance solution efficiency in topology optimization and effectively avoid the entrapment in local optima.This paper proposes a hybrid classical-quantum computing framework to solve the stress-constrained topology optimization problem for truss structures.Initially,structural analyses are performed on a classical computer to determine the stresses of truss members.Then,the optimization problem is formulated through incremental updates of member cross-sectional areas to make it compatible with a quantum annealer.The update strategy consists of a directional-control function and a magnitude-control function.By embedding stress constraints directly into the directional-control function,the original optimization problem is reformulated as a quadratic unconstrained binary optimization model suitable for quantum annealing.To realize a balance between solution accuracy and iteration efficiency,a dynamic strategy for adjusting the magnitude of area increments is proposed.Thus,the quantum annealer can effectively achieve the optimal solutions.When only the access time of the quantum processing unit is considered,the results from 2D and 3D examples of truss topology optimization validate the effectiveness of the proposed framework,and demonstrate the great potential of quantum computing in structural optimization. 展开更多
关键词 Topology optimization Truss structures Quantum computing Quantum annealing algorithm Quadratic unconstrained binary optimization problem
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A Survey of Analysis on Quantum Algorithms for Communication 认领 引用
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作者 Huang Yuhong Cui Chunfeng +5 位作者 Pan Chengkang Hou Shuai Sun Zhiwen Lu Xian Li Xinying Yuan Yifei 《China Communications》 SCIE EI CSCD 2025年第6期1-23,共23页
Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology,including communication.In this review,we discuss the current state of quantum computing in comm... Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology,including communication.In this review,we discuss the current state of quantum computing in communication and its potential applications in various areas such as network optimization,signal processing,and machine learning for communication.First,the basic principle of quantum computing,quantum physics systems,and quantum algorithms are analyzed.Then,based on the classification of quantum algorithms,several important basic quantum algorithms,quantum optimization algorithms,and quantum machine learning algorithms are discussed in detail.Finally,the basic ideas and feasibility of introducing quantum algorithms into communications are emphatically analyzed,which provides a reference to address computational bottlenecks in communication networks. 展开更多
关键词 network optimization physical system quantum computing quantum machine learning quantum optimization algorithm signal processing
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A variational quantum algorithm for the Poisson equation based on the banded Toeplitz systems 认领 引用
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作者 Xiaoqi Liu Yuedi Qu +1 位作者 Ming Li Shu-Qian Shen 《Communications in Theoretical Physics》 SCIE CAS CSCD 2025年第4期23-33,共11页
To solve the Poisson equation it is usually possible to discretize it into solving the corresponding linear system Ax=b.Variational quantum algorithms(VQAs)for the discretized Poisson equation have been studied before... To solve the Poisson equation it is usually possible to discretize it into solving the corresponding linear system Ax=b.Variational quantum algorithms(VQAs)for the discretized Poisson equation have been studied before.We present a VQA based on the banded Toeplitz systems for solving the Poisson equation with respect to the structural features of matrix A.In detail,we decompose the matrices A and A2into a linear combination of the corresponding banded Toeplitz matrix and sparse matrices with only a few non-zero elements.For the one-dimensional Poisson equation with different boundary conditions and the d-dimensional Poisson equation with Dirichlet boundary conditions,the number of decomposition terms is less than that reported in[Phys.Rev.A 2023108,032418].Based on the decomposition of the matrix,we design quantum circuits that efficiently evaluate the cost function.Additionally,numerical simulation verifies the feasibility of the proposed algorithm.Finally,the VQAs for linear systems of equations and matrix-vector multiplications with the K-banded Toeplitz matrix TnKare given,where TnK∈Rn×nand K∈O(ploylogn). 展开更多
关键词 variational quantum algorithm Poisson equation quantum circuit
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Efficient implementation of quantum permutation algorithm using a polar SrO molecule in pendular states 认领 引用
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作者 Jie-Ru Hu Zuo-Yuan Zhang Jin-Ming Liu 《Communications in Theoretical Physics》 SCIE CAS CSCD 2025年第2期39-51,共13页
Quantum algorithms offer more enhanced computational efficiency in comparison to their classical counterparts when solving specific tasks.In this study,we implement the quantum permutation algorithm utilizing a polar ... Quantum algorithms offer more enhanced computational efficiency in comparison to their classical counterparts when solving specific tasks.In this study,we implement the quantum permutation algorithm utilizing a polar molecule within an external electric field.The selection of the molecular qutrit involves the utilization of field-dressed states generated through the pendular modes of SrO.Through the application of multi-target optimal control theory,we strategically design microwave pulses to execute logical operations,including Fourier transform,oracle Ufoperation,and inverse Fourier transform within a three-level molecular qutrit structure.The observed high fidelity of our outcomes is intricately linked to the concept of the quantum speed limit,which quantifies the maximum speed of quantum state manipulation.Subsequently,we design the optimized pulse sequence to successfully simulate the quantum permutation algorithm on a single SrO molecule,achieving remarkable fidelity.Consequently,a quantum circuit comprising a single qutrit suffices to determine permutation parity with just a single function evaluation.Therefore,our results indicate that the optimal control theory can be well applied to the quantum computation of polar molecular systems. 展开更多
关键词 polar molecule optimal control quantum permutation algorithm pendular states
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Quantum-Optimization-Based Clustering and Routing Protocols for Energy-Efficient,Scalable Wireless Sensor Networks 认领 引用
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作者 Amjad Rehman Tariq Mahmood +1 位作者 Faten S.Alamri Muhammad I.Khan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1352-1394,共43页
The rapid deployment of Wireless Sensor Networks(WSNs)faces critical challenges due to sensor nodes’limited energy and communication capabilities,which restrict network lifetime and data transmission efficiency.Tradi... The rapid deployment of Wireless Sensor Networks(WSNs)faces critical challenges due to sensor nodes’limited energy and communication capabilities,which restrict network lifetime and data transmission efficiency.Traditional clustering and routing protocols often lead to unbalanced energy consumption and uneven load distribution,whereas intelligent optimization approaches are hindered by high computational costs and slow convergence.This research formulates the clustering and routing problems in WSNs as an optimization challenge under resource and energy constraints,aiming to improve stability,energy efficiency,and throughput.This research proposed three quantum optimization-based solutions to address complex issues.First,a Quantum Genetic-Enhanced K-means(QGE-K)protocol addresses inaccurate cluster-head initialization by adaptively determining the optimal number of clusters and selecting energy-balanced cluster heads,thereby improving clustering accuracy and routing efficiency.Second,a Fuzzy-Enhanced Quantum Annealing Algorithm(FEQA)protocol integrates fuzzy inference with quantum tunneling dynamics to select cluster heads and compute the most energy-efficient routing paths,extending the network lifetime in large-scale deployments.Third,a Quantum-Enhanced Particle Swarm Clustering and Routing(QE-PSCR)protocol encodes clustering and routing into a single optimization particle,employing chaotic mapping and Levy flight strategies to accelerate convergence and escape local optima,thereby reducing computation overhead.The simulation results demonstrate that all three protocols achieve significant improvements in energy consumption,load balance,throughput,and overall network lifetime.The proposedmethods apply to domains such as environmental monitoring,the industrial Internet ofThings,and military security,highlighting both theoretical contributions and practical value in advancing energy-efficientWSN design. 展开更多
关键词 Wireless sensor networks quantumgenetic-enhanced K-means quantum annealing algorithm chaotic mapping energy consumption load balancing
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Quantum Genetic Algorithm Based Ensemble Learning for Detection of Atrial Fibrillation Using ECG Signals 认领 引用 被引量：1
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作者 Yazeed Alkhrijah Marwa Fahim +4 位作者 Syed Muhammad Usman Qasim Mehmood Shehzad Khalid Mohamad A.Alawad Haya Aldossary 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期2339-2355,共17页
Atrial Fibrillation(AF)is a cardiac disorder characterized by irregular heart rhythms,typically diagnosed using Electrocardiogram(ECG)signals.In remote regions with limited healthcare personnel,automated AF detection ... Atrial Fibrillation(AF)is a cardiac disorder characterized by irregular heart rhythms,typically diagnosed using Electrocardiogram(ECG)signals.In remote regions with limited healthcare personnel,automated AF detection is extremely important.Although recent studies have explored various machine learning and deep learning approaches,challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification.In this study,we propose a novel framework that integrates robust preprocessing,comprehensive feature extraction,and an ensemble classification strategy.In the first step,ECG signals are divided into equal-sized segments using a 5-s sliding window with 50%overlap,followed by bandpass filtering between 0.5 and 45 Hz for noise removal.After preprocessing,both time and frequency-domain features are extracted,and a custom one-dimensional Convolutional Neural Network—Bidirectional Long Short-Term Memory(1D CNN-BiLSTM)architecture is introduced.Handcrafted and automated features are concatenated into a unified feature vector and classified using Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM)models.A Quantum Genetic Algorithm(QGA)optimizes weighted averages of the classifier outputs for multi-class classification,distinguishing among AF,noisy,normal,and other rhythms.Evaluated on the PhysioNet 2017 Cardiology Challenge dataset,the proposed method achieved an accuracy of 94.40%and an F1-score of 92.30%,outperforming several state-of-the-art techniques. 展开更多
关键词 Quantum genetic algorithm AF detection heart disease ECG signals CNN LSTM
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Compressed representation of quantum states via orthogonal polynomials for flow field analysis 认领 引用 被引量：2
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作者 Yu Fang Cheng Xue +7 位作者 Taiping Sun Xiaofan Xu Chuangchao Ye Tengyang Ma Huanyu Liu Yuchun Wu Zhaoyun Chen Guoping Guo 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期94-109,共16页
Quantum computing promises exponential acceleration for fluid flow simulations,yet the measurement overhead required to extract classical information from the resulting quantum states fundamentally undermines this adv... Quantum computing promises exponential acceleration for fluid flow simulations,yet the measurement overhead required to extract classical information from the resulting quantum states fundamentally undermines this advantage—a challenge termed the“output problem”.To address this,we propose an orthogonal-polynomial-based quantum neural network(OP-QNN)that generates a compressed,low-dimensional representation of these states,enabling the efficient extraction of classical information with significantly reduced measurement overhead.Within OP-QNN,we develop an orthogonal-polynomial-based variational quantum circuit as a core component,which embeds trainable parameters into orthogonal basis transformations to enhance expressivity and generate compressed coefficients.We evaluate the compressed representation through two critical post-processing tasks on fluid flow data:reconstruction and classification,demonstrating exceptional performance in both areas.The high reconstruction fidelity confirms that the compressed data preserves the state’s global structure,while the high classification accuracy proves that it retains key discriminative features.Achieved with significantly reduced computational complexity and parameter counts compared to benchmarks,these results validate OP-QNN as an effective solution to the output problem—bridging quantum simulation outputs with practical fluid analysis and offering a scalable pathway to exploit quantum advantages in computational fluid dynamics. 展开更多
关键词 Quantum computing Computational fluid dynamics Orthogonal polynomials Dimensionality reduction Variational quantum algorithm
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Exact quantum algorithm for unit commitment optimization based on partially connected quantum neural networks 认领 引用
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作者 Jian Liu Xu Zhou +1 位作者 Zhuojun Zhou Le Luo 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第10期303-312,共10页
The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum(NISQ)era.The unit commitment(UC)problem is a f... The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum(NISQ)era.The unit commitment(UC)problem is a fundamental problem in the field of power systems that aims to satisfy the power balance constraint with minimal cost.In this paper,we focus on the implementation of the UC solution using exact quantum algorithms based on the quantum neural network(QNN).This method is tested with a ten-unit system under the power balance constraint.In order to improve computing precision and reduce network complexity,we propose a knowledge-based partially connected quantum neural network(PCQNN).The results show that exact solutions can be obtained by the improved algorithm and that the depth of the quantum circuit can be reduced simultaneously. 展开更多
关键词 quantum computing quantum algorithm unit commitment quantum neural network noisy intermediate-scale quantum era
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Variational quantum algorithm for designing quantum information maskers 认领 引用
14
作者 Jin-Ze Li Ming-Hao Wang Bin Zhou 《Communications in Theoretical Physics》 SCIE CAS CSCD 2025年第3期66-74,共9页
Since the concept of quantum information masking was proposed by Modi et al(2018 Phys.Rev.Lett.120,230501),many interesting and significant results have been reported,both theoretically and experimentally.However,desi... Since the concept of quantum information masking was proposed by Modi et al(2018 Phys.Rev.Lett.120,230501),many interesting and significant results have been reported,both theoretically and experimentally.However,designing a quantum information masker is not an easy task,especially for larger systems.In this paper,we propose a variational quantum algorithm to resolve this problem.Specifically,our algorithm is a hybrid quantum-classical model,where the quantum device with adjustable parameters tries to mask quantum information and the classical device evaluates the performance of the quantum device and optimizes its parameters.After optimization,the quantum device behaves as an optimal masker.The loss value during optimization can be used to characterize the performance of the masker.In particular,if the loss value converges to zero,we obtain a perfect masker that completely masks the quantum information generated by the quantum information source,otherwise,the perfect masker does not exist and the subsystems always contain the original information.Nevertheless,these resulting maskers are still optimal.Quantum parallelism is utilized to reduce quantum state preparations and measurements.Our study paves the way for wide application of quantum information masking,and some of the techniques used in this study may have potential applications in quantum information processing. 展开更多
关键词 variational quantum algorithm quantum information masking quantum parallelism
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Variational Quantum Algorithm for Solving the Liouvillian Gap 认领 引用
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作者 Xu-Dan Xie Zheng-Yuan Xue Dan-Bo Zhang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第8期121-128,共8页
In open quantum systems,the Liouvillian gap characterizes the relaxation time toward the steady state.However,accurately computing this quantity is notoriously difficult due to the exponential growth of the Hilbert sp... In open quantum systems,the Liouvillian gap characterizes the relaxation time toward the steady state.However,accurately computing this quantity is notoriously difficult due to the exponential growth of the Hilbert space and the non-Hermitian nature of the Liouvillian superoperator.In this work,we propose a variational quantum algorithm for efficiently estimating the Liouvillian gap.By utilizing the Choi-Jamio lkowski isomorphism,we reformulate the problem as finding the first excitation energy of an effective non-Hermitian Hamiltonian.Our method employs variance minimization with an orthogonality constraint to locate the first excited state and adopts a two-stage optimization scheme to enhance convergence.Moreover,to address scenarios with degenerate steady states,we introduce an iterative energy-offset scanning technique.Numerical simulations on the dissipative XXZ model confirm the accuracy and robustness of our algorithm across a range of system sizes and dissipation strengths.These results demonstrate the promise of variational quantum algorithms for simulating open quantum many-body systems on near-term quantum hardware. 展开更多
关键词 open quantum systems liouvillian gap relaxation time toward steady statehoweveraccurately hilbert space choi jamio lokia isomorphism finding first excitation energy variational quantum algorithm
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Progressive quantum algorithm for maximum independent set with quantum alternating operator ansatz 认领 引用
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作者 Xiao-Hui Ni Ling-Xiao Li +3 位作者 Yan-Qi Song Zheng-Ping Jin Su-Juan Qin Fei Gao 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第7期75-87,共13页
The quantum alternating operator ansatz algorithm(QAOA+)is widely used for constrained combinatorial optimization problems(CCOPs)due to its ability to construct feasible solution spaces.In this paper,we propose a prog... The quantum alternating operator ansatz algorithm(QAOA+)is widely used for constrained combinatorial optimization problems(CCOPs)due to its ability to construct feasible solution spaces.In this paper,we propose a progressive quantum algorithm(PQA)to reduce qubit requirements for QAOA+in solving the maximum independent set(MIS)problem.PQA iteratively constructs a subgraph likely to include the MIS solution of the original graph and solves the problem on it to approximate the global solution.Specifically,PQA starts with a small-scale subgraph and progressively expands its graph size utilizing heuristic expansion strategies.After each expansion,PQA solves the MIS problem on the newly generated subgraph using QAOA+.In each run,PQA repeats the expansion and solving process until a predefined stopping condition is reached.Simulation results show that PQA achieves an approximation ratio of 0.95 using only 5.57%(2.17%)of the qubits and 17.59%(6.43%)of the runtime compared with directly solving the original problem with QAOA+on Erd?s-Rényi(3-regular)graphs,highlighting the efficiency and scalability of PQA. 展开更多
关键词 quantum alternating operator ansatz algorithm(QAOA+) constrained combinatorial optimization problems(CCOPs) maximum independent set(MIS) feasible space
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Random State Approach to Quantum Computation of Electronic-Structure Properties 认领 引用
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作者 Yiran Bai Feng Xiong Xueheng Kuang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第1期89-104,共16页
Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and v... Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials. 展开更多
关键词 periodic materials random state circuit random state quantum algorithms electronic structure properties density states aperiodic materials quantum algorithms quantum computation
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Hierarchical QAOA circuit design framework for distributed quantum computing 认领 引用
18
作者 Ting-Yu Luo Yu-Xin Deng 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期311-327,共17页
The quantum approximate optimization algorithm(QAOA)is a promising approach for solving combinatorial optimization problems on real quantum devices.As QAOA scales to tackle larger problem instances,the limited qubit c... The quantum approximate optimization algorithm(QAOA)is a promising approach for solving combinatorial optimization problems on real quantum devices.As QAOA scales to tackle larger problem instances,the limited qubit capacity of single-chip systems becomes a critical bottleneck.To overcome this limitation,distributed quantum computing(DQC)provides a scalable solution.However,when QAOA circuits are executed in such systems,their performance is significantly hindered by the high cost of remote communication.Motivated by this challenge,we propose HiQ-DF,a QAOA circuit design framework tailored for DQC systems.By employing a hierarchical optimization strategy,HiQ-DF enables comprehensive multi-objective optimization during circuit construction.Experimental results on QAOA circuits solving MaxCut instances show that our framework significantly outperforms baseline methods,achieving an average reduction of 26.12% in EPR pair usage(up to 36.85%),26.44% in circuit latency(up to 35.27%),and 39.63% in circuit depth(up to49.3%). 展开更多
关键词 quantum circuit design quantum approximate optimization algorithm distributed quantum computing
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Numbering and Generating Quantum Algorithms 认领 引用
19
作者 Mohamed A. El-Dosuky 《Journal of Computer and Communications》 2025年第2期126-141,共16页
Quantum computing offers unprecedented computational power, enabling simultaneous computations beyond traditional computers. Quantum computers differ significantly from classical computers, necessitating a distinct ap... Quantum computing offers unprecedented computational power, enabling simultaneous computations beyond traditional computers. Quantum computers differ significantly from classical computers, necessitating a distinct approach to algorithm design, which involves taming quantum mechanical phenomena. This paper extends the numbering of computable programs to be applied in the quantum computing context. Numbering computable programs is a theoretical computer science concept that assigns unique numbers to individual programs or algorithms. Common methods include Gödel numbering which encodes programs as strings of symbols or characters, often used in formal systems and mathematical logic. Based on the proposed numbering approach, this paper presents a mechanism to explore the set of possible quantum algorithms. The proposed approach is able to construct useful circuits such as Quantum Key Distribution BB84 protocol, which enables sender and receiver to establish a secure cryptographic key via a quantum channel. The proposed approach facilitates the process of exploring and constructing quantum algorithms. 展开更多
关键词 Quantum Algorithms Numbering Computable Programs Quantum Key Distribution
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Preparation of digital-encoded and analog-encoded quantum states corresponding to matrix operations 认领 引用
20
作者 Kaitian Gao Youlong Yang Zhenye Du 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第1期332-344,共13页
Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prereq... Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k1√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k2√1·poly log(mnl/ε)),where k1and k2are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time. 展开更多
关键词 quantum algorithm matrix operation digital and analog-encoded states quantum computing
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