期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到163篇文章
< 1 2 … 9 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Editorial:Quantum algorithms in computational mechanics 认领 引用
1
《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
暂未订购 下载PDF
A Survey of Analysis on Quantum Algorithms for Communication 认领 引用
2
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
暂未订购 下载PDF
Numbering and Generating Quantum Algorithms 认领 引用
3
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
暂未订购 下载PDF
Quantum Algorithms and Experiment Implementations Based on IBM Q 认领 引用
4
作者 Wenjie Liu Junxiu Chen +3 位作者 Yinsong Xu Jiahao Tang Lian Tong Xiaoyu Song 《Computers, Materials & Continua》 SCIE EI 2020年第11期1671-1689,共19页
With the rapid development of quantum theory and technology in recent years,especially the emergence of some quantum cloud computing platforms,more and more researchers are not satisfied with the theoretical derivatio... With the rapid development of quantum theory and technology in recent years,especially the emergence of some quantum cloud computing platforms,more and more researchers are not satisfied with the theoretical derivation and simulation verification of quantum computation(especially quantum algorithms),experimental verification on real quantum devices has become a new trend.In this paper,three representative quantum algorithms,namely Deutsch-Jozsa,Grover,and Shor algorithms,are briefly depicted,and then their implementation circuits are presented,respectively.We program these circuits on python with QISKit to connect the remote real quantum devices(i.e.,ibmqx4,ibmqx5)on IBM Q to verify these algorithms.The experimental results not only show the feasibility of these algorithms,but also serve to evaluate the functionality of these devices. 展开更多
关键词 Quantum algorithms implementation circuit IBM Q QISKit program
暂未订购 下载PDF
Quantum Algorithms for Some Well—Known NP Problems 认领 引用 被引量：1
5
作者 GUOHao LONGGui－Lu 等 《Communications in Theoretical Physics》 SCIE CAS 2002年第4期424-426,共3页
It is known that quantum computer is more powerful than classical computer.In this paper we present quantum algorithms for some famous NP problems in graph theory and combination theory,these quantum algorithms are at... It is known that quantum computer is more powerful than classical computer.In this paper we present quantum algorithms for some famous NP problems in graph theory and combination theory,these quantum algorithms are at least quadratically faster than the classical ones. 展开更多
关键词 quantum algorithms NP problem graph theory combination theory
暂未订购 下载PDF
Application of quantum algorithms to direct measurement of concurrence of a two-qubit pure state 认领 引用
6
作者 王洪福 张寿 《Chinese Physics B》 SCIE EI CAS 2009年第7期2642-2648,共7页
This paper proposes a method to measure directly the concurrence of an arbitrary two-qubit pure state based on a generalized Grover quantum iteration algorithm and a phase estimation algorithm. The concurrence can be ... This paper proposes a method to measure directly the concurrence of an arbitrary two-qubit pure state based on a generalized Grover quantum iteration algorithm and a phase estimation algorithm. The concurrence can be calculated by applying quantum algorithms to two available copies of the bipartite system, and a final measurement on the auxiliary working qubits gives a better estimation of the concurrence. This method opens new prospects of entanglement measure by the application of quantum algorithms. The implementation of the protocol would be an important step toward quantum information processing and more complex entanglement measure of the finite-dimensional quantum system with an arbitrary number of qubits. 展开更多
关键词 concurrence quantum algorithm entanglement measure
暂未订购 下载PDF
Quantum algorithms for matrix operations and linear systems of equations 认领 引用 被引量：3
7
作者 Wentao Qi Alexandr I Zenchuk +1 位作者 Asutosh Kumar Junde Wu 《Communications in Theoretical Physics》 SCIE CAS CSCD 2024年第3期100-112,共13页
Fundamental matrix operations and solving linear systems of equations are ubiquitous in scientific investigations.Using the‘sender-receiver’model,we propose quantum algorithms for matrix operations such as matrix-ve... Fundamental matrix operations and solving linear systems of equations are ubiquitous in scientific investigations.Using the‘sender-receiver’model,we propose quantum algorithms for matrix operations such as matrix-vector product,matrix-matrix product,the sum of two matrices,and the calculation of determinant and inverse matrix.We encode the matrix entries into the probability amplitudes of the pure initial states of senders.After applying proper unitary transformation to the complete quantum system,the desired result can be found in certain blocks of the receiver’s density matrix.These quantum protocols can be used as subroutines in other quantum schemes.Furthermore,we present an alternative quantum algorithm for solving linear systems of equations. 展开更多
关键词 matrix operation systems of linear equations ‘sender-receiver’quantum computation model quantum algorithm
暂未订购 下载PDF
Variational quantum algorithms for trace norms and their applications 认领 引用
8
作者 Sheng-Jie Li Jin-Min Liang +1 位作者 Shu-Qian Shen Ming Li 《Communications in Theoretical Physics》 SCIE CAS CSCD 2021年第10期90-96,共7页
The trace norm of matrices plays an important role in quantum information and quantum computing. How to quantify it in today’s noisy intermediate scale quantum(NISQ) devices is a crucial task for information processi... The trace norm of matrices plays an important role in quantum information and quantum computing. How to quantify it in today’s noisy intermediate scale quantum(NISQ) devices is a crucial task for information processing. In this paper, we present three variational quantum algorithms on NISQ devices to estimate the trace norms corresponding to different situations.Compared with the previous methods, our means greatly reduce the requirement for quantum resources. Numerical experiments are provided to illustrate the effectiveness of our algorithms. 展开更多
关键词 quantum algorithm trace norm variational algorithm
暂未订购 下载PDF
Variational quantum algorithms with invariant probabilistic error cancellation on noisy quantum processors 认领 引用
9
作者 Yulin Chi Hongyi Shi +8 位作者 Wen Zheng Haoyang Cai Yu Zhang Xinsheng Tan Shaoxiong Li Jianwei Wang Jiangyu Cui Man-Hong Yung Yang Yu 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CSCD 2026年第1期162-174,共13页
In the noisy intermediate-scale quantum era,emerging classical-quantum hybrid optimization algorithms,such as variational quantum algorithms(VQAs),can leverage the unique characteristics of quantum devices to accelera... In the noisy intermediate-scale quantum era,emerging classical-quantum hybrid optimization algorithms,such as variational quantum algorithms(VQAs),can leverage the unique characteristics of quantum devices to accelerate computations tailored to specific problems with shallow circuits.However,these algorithms encounter biases and iteration difficulties due to significant noise in quantum processors.These difficulties can only be partially addressed without error correction by optimizing hardware,reducing circuit complexity,or fitting and extrapolating.A compelling solution is applying probabilistic error cancellation(PEC),a quantum error mitigation technique that enables unbiased results without full error correction.Traditional PEC is challenging to apply in VQAs due to its variance amplification,contradicting iterative process assumptions.This paper proposes a novel noise-adaptable strategy that combines PEC with the quantum approximate optimization algorithm(QAOA).It is implemented through invariant sampling circuits(invariant-PEC,or IPEC)and substantially reduces iteration variance.This strategy marks the first successful integration of PEC and QAOA,resulting in efficient convergence.Moreover,we introduce adaptive partial PEC(APPEC),which modulates the error cancellation proportion of IPEC during iteration.We experimentally validate this technique on a superconducting quantum processor,cutting sampling cost by 90.1%.Notably,we find that dynamic adjustments of error levels via APPEC can enhance the ability to escape from local minima and reduce sampling costs.These results open promising avenues for executing VQAs with large-scale,low-noise quantum circuits,paving the way for practical quantum computing advancements. 展开更多
关键词 variational quantum algorithms probabilistic error cancellation quantum approximate optimization algorithm
暂未订购 下载PDF
Random State Approach to Quantum Computation of Electronic-Structure Properties 认领 引用
10
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
暂未订购 下载PDF
Quantum computing-enhanced topology optimization with stress constraints for truss structures 认领 引用 被引量：2
11
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
暂未订购 下载PDF
Quantum algorithms for uncertainty quantification:Applications to partial differential equations 认领 引用
12
作者 Francoise Golse Shi Jin Nana Liu 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CSCD 2025年第10期34-55,共22页
Most problems in uncertainty quantification,despite their ubiquitousness in scientific computing,applied mathematics and data science,remain formidable on a classical computer.For uncertainties that arise in partial d... Most problems in uncertainty quantification,despite their ubiquitousness in scientific computing,applied mathematics and data science,remain formidable on a classical computer.For uncertainties that arise in partial differential equations(PDEs),large numbers M>>1 of samples are required to obtain accurate ensemble averages.This usually involves solving the PDE M times.In addition,to characterise the stochasticity in a PDE,the dimension L of the random input variables is high in most cases,and classical algorithms suffer from the curse-of-dimensionality.We propose new quantum algorithms for PDEs with uncertain coefficients that are more efficient in M and L in various important regimes,compared to their classical counterparts.We introduce transformations that convert the original d-dimensional equation(with uncertain coefficients)into d+L(for dissipative equations)or d+2L(for wave type equations)dimensional equations(with certain coefficients)in which the uncertainties appear only in the initial data.These transformations also allow one to superimpose the M different initial data,so the computational cost for the quantum algorithm to obtain the ensemble average from M different samples is independent of M,while also showing potential advantage in d,L and precisionεin computing ensemble averaged solutions or physical observables. 展开更多
关键词 partial differential equations quantum algorithm uncertainty quantification
暂未订购 下载PDF
A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
13
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
暂未订购 下载PDF
Quantum-Optimization-Based Clustering and Routing Protocols for Energy-Efficient,Scalable Wireless Sensor Networks 认领 引用
14
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
暂未订购 下载PDF
Compressed representation of quantum states via orthogonal polynomials for flow field analysis 认领 引用 被引量：2
15
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
暂未订购 下载PDF
Physics Informed Hybrid Quantum-Classical Dispatching for LargeScale Renewable Power Systems:A Noise-Resilient Framework 认领 引用
16
作者 Fu Zhang Yuming Zhao 《Journal of Electronic Research and Application》 2026年第3期44-53,共10页
Rising renewable penetration introduces severe non-convexity in power dispatching,straining classical optimization.While variational quantum algorithms(VQAs)on NISQ devices offer combinatorial potential,“black-box”a... Rising renewable penetration introduces severe non-convexity in power dispatching,straining classical optimization.While variational quantum algorithms(VQAs)on NISQ devices offer combinatorial potential,“black-box”approaches struggle with scalability and grid constraints.We propose the physics-informed hybrid quantum-classical dispatching(PI-HQCD)framework to address these limitations.PI-HQCD maps power flow and storage constraints directly into a topology-aware Hamiltonian,shrinking the search space.A noise-adaptive regularization technique bounds the objective’s Lipschitz constant,ensuring convergence under measurement noise.Experiments on IEEE 39-bus and 118-bus systems show PI-HQCD outperforms stochastic dual dynamic programming(SDDP)in cost and renewable utilization.Theoretical analysis confirms our topology-aligned ansatz achieves gradient variance scaling,mitigating barren plateaus.This work bridges physical laws and quantum algorithms for next-generation grid operations. 展开更多
关键词 Hybrid quantum-classical optimization Physics-informed learning Renewable power dispatch Variational quantum algorithms Noise resilience
暂未订购 下载PDF
Deep Variational Quantum Circuits with Barren-Plateau-Free Architectures 认领 引用
17
作者 Kaining Zhang Min-Hsiu Hsieh Dacheng Tao 《Artificial Intelligence Science and Engineering》 2026年第1期66-84,共19页
Variational quantum algorithms(VQAs)with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase.This result leads to a general belief th... Variational quantum algorithms(VQAs)with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase.This result leads to a general belief that a deep circuit will not be feasible.In this work,we provide a viable solution to the vanishing gradient problem for deep VQAs with theoretical guarantees.Specifically,we prove that for quantum controlled-layer and quantum residual network(QResNet),architectures,the expectation of the gradient norm can be lower bounded by a value that is independent of the qubit number and the circuit depth.Our results follow from a careful analysis of the gradient behavior on parameter space consisting of rotation angles,as employed in almost all VQAs,instead of relying on impractical 2-design assumptions.We conduct several numerical experiments as verifications,where only our circuits are trainable and converge,while hardware-efficient and random circuits with similar number of parameters in comparison cannot converge. 展开更多
关键词 variational quantum algorithms quantum machine learning barren plateaus
暂未订购 下载PDF
Enhanced multiscale quantum approximate optimization algorithm in multibody combinatorial optimization problems 认领 引用
18
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
暂未订购 下载PDF
Hierarchical QAOA circuit design framework for distributed quantum computing 认领 引用
19
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
暂未订购 下载PDF
Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation 认领 引用 被引量：20
20
作者 He-Liang Huang Xiao-Yue Xu +5 位作者 Chu Guo Guojing Tian Shi-Jie Wei Xiaoming Sun Wan-Su Bao Gui-Lu Long 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2023年第5期23-72,共50页
Quantum computing is a game-changing technology for global academia,research centers and industries including computational science,mathematics,finance,pharmaceutical,materials science,chemistry and cryptography.Altho... Quantum computing is a game-changing technology for global academia,research centers and industries including computational science,mathematics,finance,pharmaceutical,materials science,chemistry and cryptography.Although it has seen a major boost in the last decade,we are still a long way from reaching the maturity of a full-fledged quantum computer.That said,we will be in the noisy-intermediate scale quantum(NISQ)era for a long time,working on dozens or even thousands of qubits quantum computing systems.An outstanding challenge,then,is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise.To address this challenge,several near-term quantum computing techniques,including variational quantum algorithms,error mitigation,quantum circuit compilation and benchmarking protocols,have been proposed to characterize and mitigate errors,and to implement algorithms with a certain resistance to noise,so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications.Besides,the development of near-term quantum devices is inseparable from the efficient classical sim-ulation,which plays a vital role in quantum algorithm design and verification,error-tolerant verification and other applications.This review will provide a thorough introduction of these near-term quantum computing techniques,report on their progress,and finally discuss the future prospect of these techniques,which we hope will motivate researchers to undertake additional studies in this field. 展开更多
关键词 quantum computing noisy-intermediate scale quantum variational quantum algorithms error mitigation circuit com-pilation benchmarking protocols classical simulation
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 9 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈