检索结果-内蒙古大学图书馆

3rd IEEE International Conference on quantum Software (IEEE QSW)

作者： Volpe, Deborah Quetschlich, Nils Graziano, Mariagrazia Turvani, Giovanna Wille, Robert Politecn Torino Italy Dept Elect & Telecommun Turin Italy Tech Univ Munich Chair Design Automat Munich Germany Politecn Torino Italy Dept Appl Sci & Technol Turin Italy Software Competence Ctr Hagenberg GmbH SCCH Hagenberg im Muhlkreis Austria

ISBN: (纸本)9798350368482;9798350368475

Optimizing objective functions stands to benefit significantly from leveraging quantum computers, promising enhanced solution quality across various application domains in the future. However, harnessing the potential of quantum solvers necessitates formulating problems according to the Quadratic Unconstrained Binary optimization (QUBO) model, demanding significant expertise in quantum computation and QUBO formulations. This expertise barrier limits access to quantum solutions. Fortunately, automating the conversion of conventional optimization problems into QUBO formulations presents a solution for promoting accessibility to quantum solvers. This article addresses the unmet need for a comprehensive automatic framework to assist users in utilizing quantum solvers for optimization tasks while preserving interfaces that closely resemble conventional optimization practices. The framework prompts users to specify variables, optimization criteria, as well as validity constraints and, afterwards, allows them to choose the desired solver. Subsequently, it automatically transforms the problem description into a format compatible with the chosen solver and provides the resulting solution. Additionally, the framework offers instruments for analyzing solution validity and quality. Comparative analysis against existing libraries and tools in the literature highlights the comprehensive nature of the proposed framework. Two use cases (the knapsack problem and linear regression) are considered to show the completeness and efficiency of the framework in real-world applications. Finally, the proposed framework represents a significant advancement towards automating quantum computing solutions and widening access to quantum optimization for a broader range of users. The framework is publicly available on GitHub (https://***/cda-tum/mqt-qao) as part of the Munich quantum Toolkit (MQT).

关键词： QUBO quantum Computing Design Automation quantum optimization quantum Annealer quantum approximate optimization algorithm Variational quantum Eigensolver Grover Adaptive Search

来源：评论

学校读者我要写书评

暂无评论

Noise-Robust End-to-End quantum Control using Deep Autoregressive Policy Networks 2

Noise-Robust End-to-End Quantum Control using Deep Autoregre...

引用

2nd Annual Conference on Mathematical and Scientific Machine Learning

作者： Yao, Jiahao Kottering, Paul Gundlach, Hans Lin, Lin Bukov, Marin Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Univ Calif Berkeley Dept Phys Berkeley CA 94720 USA Univ Calif Berkeley Computat Res Div Lawrence Berkeley Natl Lab Challenge Inst Quantum Computat Berkeley CA 94720 USA St Kliment Ohridski Univ Sofia Dept Phys 5 James Bourchier Blvd Sofia 1164 Bulgaria

Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-body systems. In many applications, it is the optimization of both continuous and discrete parameters that poses a formidable challenge. Using reinforcement learning (RL), we present a hybrid policy gradient algorithm capable of simultaneously optimizing continuous and discrete degrees of freedom in an uncertainty-resilient way. The hybrid policy is modeled by a deep autoregressive neural network to capture causality. We employ the algorithm to prepare the ground state of the nonintegrable quantum Ising model in a unitary process, parametrized by a generalized quantum approximate optimization ansatz: the RL agent solves the discrete combinatorial problem of constructing the optimal sequences of unitaries out of a predefined set and, at the same time, it optimizes the continuous durations for which these unitaries are applied. We demonstrate the noise-robust features of the agent by considering three sources of uncertainty: classical and quantum measurement noise, and errors in the control unitary durations. Our work exhibits the beneficial synergy between reinforcement learning and quantum control.

关键词： quantum control quantum approximate optimization algorithm quantum computing Reinforcement learning Policy gradient Autoregressive policy network Proximal policy optimization Noise-robust optimization

来源：评论

学校读者我要写书评

暂无评论

Transferability of optimal QAOA parameters between random graphs 2

Transferability of optimal QAOA parameters between random gr...

引用

IEEE International Conference on quantum Computing and Engineering (QCE) / quantum Week

作者： Galda, Alexey Liu, Xiaoyuan Lykov, Danylo Alexeev, Yuri Safro, Ilya Menten AI Inc Palo Alto CA 94303 USA Univ Chicago James Franck Inst Chicago IL 60637 USA Argonne Natl Lab Computat Sci Div Lemont IL 60439 USA Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA

ISBN: (纸本)9781665416917

The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. In a typical QAOA setup, a set of quantum circuit parameters is optimized to prepare a quantum state used to find the optimal solution of a combinatorial optimization problem. Several empirical observations about optimal parameter concentration effects for special QAOA MaxCut problem instances have been made in recent literature, however, a rigorous study of the subject is still lacking. We show that convergence of the optimal QAOA parameters around specific values and, consequently, successful transferability of parameters between different QAOA instances can be explained and predicted based on the local properties of the graphs, specifically the types of subgraphs (lightcones) from which the graphs are composed. We apply this approach to random regular and general random graphs. For example, we demonstrate how optimized parameters calculated for a 6-node random graph can be successfully used without modification as nearly optimal parameters for a 64-node random graph, with less than 1% reduction in approximation ratio as a result. This work presents a pathway to identifying classes of combinatorial optimization instances for which such variational quantum algorithms as QAOA can be substantially accelerated.

关键词： quantum computing quantum optimization quantum approximate optimization algorithm tensor-network simulator

来源：评论

学校读者我要写书评

暂无评论

QAOA-Assisted Benders' Decomposition for Mixed-integer Linear Programming 59

QAOA-Assisted Benders' Decomposition for Mixed-integer Linea...

引用

59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Zhao, Zhongqi Fan, Lei Guo, Yuanxiong Wang, Yu Han, Zhu Hanzo, Lajos Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Univ Houston Dept Engn Technol Houston TX 77004 USA Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX USA Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

ISBN: (纸本)9781728190549

Benders' decomposition (BD) algorithm constitutes a powerful mathematical programming method of solving mixed-integer linear programming (MILP) problems with a specific block structure. Nevertheless, BD still needs to solve an NP-hard quasi-integer programming master problem (MAP), which motivates us to harness the popular variational quantum algorithm (VQA) to assist BD. More specifically, we choose the popular quantum approximate optimization algorithm (QAOA) of the VQA family. We transfer the BD's MAP into a digital quantum circuit associated with a physically tangible problem-specific ansatz, and then solve it with the aid of a state-of-the-art digital quantum computer. Next, we evaluate the computational results and discuss the feasibility of the proposed algorithm. The hybrid approach advocated, which utilizes both classical and digital quantum computers, is capable of tackling many practical MILP problems in communication and networking, as demonstrated by a pair of case studies.

关键词： Benders' Decomposition Mixed-Integer Linear Programming optimization Digital quantum Computing quantum approximate optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

quantum Variational algorithms for the Aircraft Deconfliction Problem 24th

Quantum Variational Algorithms for the Aircraft Deconflictio...

引用

24th International Conference on Computational Science (ICCS)

作者： Peeyna, Tomasz Kurowski, Krzysztof Rozycki, Rafal Waligora, Grzegorz Weglarz, Jan IBCH PAS Poznan Supercomp & Networking Ctr Poznan Poland Poznan Univ Tech Inst Comp Sci Poznan Poland

ISBN: (纸本)9783031637773;9783031637780

Tactical deconfliction problem involves resolving conflicts between aircraft to ensure safety while maintaining efficient trajectories. Several techniques exist to safely adjust aircraft parameters such as speed, heading angle, or flight level, with many relying on mixed-integer linear or nonlinear programming. These techniques, however, often encounter challenges in real-world applications due to computational complexity and scalability issues. This paper proposes a new quantum approach that applies the quantum approximate optimization algorithm (QAOA) and the quantum Alternating Operator Ansatz (QAOAnsatz) to address the aircraft deconfliction problem. We present a formula for designing quantum Hamiltonians capable of handling a broad range of discretized maneuvers, with the aim of minimizing changes to original flight schedules while safely resolving conflicts. Our experiments show that a higher number of aircraft poses fewer challenges than a larger number of maneuvers. Additionally, we benchmark the newest IBM quantum processor and show that it successfully solves four out of five instances considered. Finally, we demonstrate that incorporating hard constraints into the mixer Hamiltonian makes QAOAnsatz superior to QAOA. These findings suggest quantum algorithms could be a valuable algorithmic candidate for addressing complex optimization problems in various domains, with implications for enhancing operational efficiency and safety in aviation and other sectors.

关键词： Tactical Aircraft Deconfliction quantum approximate optimization algorithm quantum Alternating Operator Ansatz

来源：评论

学校读者我要写书评

暂无评论

An Efficient QAOA via a Polynomial QPU-Needless Approach

An Efficient QAOA via a Polynomial QPU-Needless Approach

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Chicano, Francisco Abdelmoiz Dahi, Zakaria Luque, Gabriel Univ Malaga ITIS Software Malaga Spain

ISBN: (纸本)9798400701207

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum algorithm described as ansatzes that represent both the problem and the mixer Hamiltonians. Both are parameterizable unitary transformations executed on a quantum machine/simulator and whose parameters are iteratively optimized using a classical device to optimize the problem's expectation value. To do so, in each QAOA iteration, most of the literature uses a quantum machine/simulator to measure the QAOA outcomes. However, this poses a severe bottleneck considering that quantum machines are hardly constrained (e.g. long queuing, limited qubits, etc.), likewise, quantum simulation also induces exponentiallyincreasing memory usage when dealing with large problems requiring more qubits. These limitations make today's QAOA implementation impractical since it is hard to obtain good solutions with a reasonably-acceptable time/resources. Considering these facts, this work presents a new approach with two main contributions, including (I) removing the need for accessing quantum devices or large-sized classical machines during the QAOA optimization phase, and (II) ensuring that when dealing with some k-bounded pseudo-Boolean problems, optimizing the exact problem's expectation value can be done in polynomial time using a classical computer.

关键词： quantum approximate optimization algorithm Combinatorial optimization quantum Computing

来源：评论

学校读者我要写书评

暂无评论

Error Mitigation for Deep quantum optimization Circuits by Leveraging Problem Symmetries 2

Error Mitigation for Deep Quantum Optimization Circuits by L...

引用

IEEE International Conference on quantum Computing and Engineering (QCE) / quantum Week

作者： Shaydulin, Ruslan Galda, Alexey Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Menten AI Inc Palo Alto CA 94303 USA Univ Chicago James Franck Inst Chicago IL 60637 USA Argonne Natl Lab Computat Sci Div Lemont IL 60439 USA

ISBN: (纸本)9781665416917

High error rates and limited fidelity of quantum gates in near-term quantum devices are the central obstacles to successful execution of the quantum approximate optimization algorithm (QAOA). In this paper we introduce an application-specific approach for mitigating the errors in QAOA evolution by leveraging the symmetries present in the classical objective function to be optimized. Specifically, the QAOA state is projected into the symmetry-restricted subspace, with projection being performed either at the end of the circuit or throughout the evolution. Our approach improves the fidelity of the QAOA state, thereby increasing both the accuracy of the sample estimate of the QAOA objective and the probability of sampling the binary string corresponding to that objective value. We demonstrate the efficacy of the proposed methods on QAOA applied to the MaxCut problem, although our methods are general and apply to any objective function with symmetries, as well as to the generalization of QAOA with alternative mixers. We experimentally verify the proposed methods on an IBM quantum processor, utilizing up to 5 qubits. When leveraging a global bit-flip symmetry, our approach leads to a 23% average improvement in quantum state fidelity.

关键词： quantum computing error mitigation quantum optimization quantum approximate optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Combinatorial optimization with variational approaches on noisy quantum devices

Combinatorial optimization with variational approaches on no...

引用

Annual IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

作者： Miki, Tsukasa Okita, Ryo Shimada, Moe Shirakashi, Jun-ichi Tokyo Univ Agr & Technol Dept Elect & Elect Engn Tokyo Japan

ISBN: (纸本)9781665448819

Variational quantum eigensolver (VQE), a kind of quantum-classical hybrid method, has latent powers to leverage noisy quantum devices by effectively managing a limited number of qubits with finite coherent times. We implemented VQE with real quantum processors through IBM quantum Experience and applied it to combinatorial optimizations. Notably, a short-depth quantum circuit consisting of a lower number of quantum gate than that in the quantum approximate optimization algorithm can search the ground states of diagonal Hamiltonians encoding the problems. Since the simplest case of the quantum circuit for VQE requires only single-qubit gates, it can be realized using current quantum computers.

关键词： noisy intermediate-scale quantum device quantum-classical hybrid algorithm variational quantum eigensolver quantum approximate optimization algorithm variational principle

来源：评论

学校读者我要写书评

暂无评论

quantum approximate optimization of the coset leader problem for binary linear codes

COMPUTATIONAL AND MATHEMATICAL METHODS

引用

COMPUTATIONAL AND MATHEMATICAL METHODS 2021年第6期3卷

作者： Epelde, Markel Combarro, Elias F. Rua, Ignacio F. Univ Basque Country Biscay Spain Univ Oviedo Dept Comp Sci Asturias Spain Univ Oviedo Dept Math Asturias Spain

The security of a broad family of coding-based cryptographic techniques relies on the hardness of the Syndrome Decoding Problem (SDP). In this problem, the aim is to find a word with a given syndrome and of Hamming weight smaller than a prefixed bound. If this last condition is replaced by "of minimum weight," then we have the Coset Leader Problem (CLP), being Finding Low Weight Codewords (FLWC) a particular case (when the zero syndrome is considered). An algorithm that has been proposed in order to obtain approximate solutions of problems of these kind (NP-complete) is the quantum approximate optimization algorithm (QAOA), a variational hybrid quantum-classical algorithm. In this paper, we apply the QAOA to the CLP for binary linear codes. We model the problem, make the theoretical analysis the case of the first level, and introduce some experiments to test its performance. The experiments have been carried out on quantum computer simulators with codes of different lengths and QAOA of different depth.

关键词： coset leader problem quantum approximate optimization algorithm syndrome decoding problem

来源：评论

学校读者我要写书评

暂无评论

Hamiltonian switching control of noisy bipartite qubit systems

引用

NEW JOURNAL OF PHYSICS 2024年第2期26卷 023051页

作者： Yang, Zhibo Kosut, Robert L. Whaley, K. Birgitta Univ Calif Berkeley Dept Chem Berkeley CA 94720 USA Berkeley Ctr Quantum Informat & Computat Berkeley CA 94720 USA SC Solut Sunnyvale CA 94085 USA Princeton Univ Dept Chem Princeton NJ USA

We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the quantum approximate optimization algorithm, to mitigate environmental noise on qubits. We demonstrate the control for a central spin coupled to bath spins via isotropic Heisenberg interactions, and then make physical applications to the protection of quantum gates performed on superconducting transmon qubits coupling to environmental two-level-systems (TLSs) through dipole-dipole interactions, as well as on such qubits coupled to both TLSs and a Lindblad bath. The control field is classical and acts only on the system qubits. We use reinforcement learning with policy gradient to optimize the Hamiltonian switching control protocols, using a fidelity objective for specific target quantum gates. We use this approach to demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) in the majority of our test cases and showing improvement beyond this to values of 0.999 999 999 (nine nines) upon a subsequent optimization by GRadient Ascent Pulse Engineering (GRAPE). We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite control of quantum gates.

关键词： quantum approximate optimization algorithm error mitigation quantum control open quantum systems spin bath two-level systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：