检索结果-内蒙古大学图书馆

19th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Baioletti, Marco Santini, Francesco Univ Perugia Dept Math & Comp Sci Perugia Italy

ISBN: (纸本)9783031208614;9783031208621

We present an encoding of some NP-Complete problems in Abstract Argumentation to quadratic unconstrained binary optimization (QUBO) problems. A solution for a QUBO problem corresponds to minimize a quadratic function over binary variables (0/1), whose coefficients can be represented by a symmetric square matrix. Being this problem NP-Complete as well, there exist approximate solvers: from the D-Wave Ocean SDK we test a simulated annealing algorithm and a real quantum annelaer provided by the Leap (TM) Quantum Cloud Service. Hence, we propose a new encoding for Abstract Argumentation problems, which will benefit from the future development of quantum computation.

关键词： Abstract argumentation Computational model quadratic unconstrained binary optimization Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Simulations of Quantum Approximate optimization Algorithm on HPC-QC Integrated Systems 5

Simulations of Quantum Approximate Optimization Algorithm on...

引用

2024 International Conference on Quantum Computing and Engineering

作者： Kim, Seongmin Suh, In-Saeng Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN 37380 USA

ISBN: (纸本)9798331541378

The Quantum Approximate optimization Algorithm (QAOA) has emerged as a promising tool for accelerating optimization processes in the Noisy Intermediate-Scale Quantum (NISQ) era. Compared to classical methods, QAOA efficiently solves optimization problems, often formulated as quadratic unconstrained binary optimization (QUBO) problems. Classical quantum simulators are crucial for evaluating quantum algorithms due to limited quantum resources. However, QAOA's performance can vary with different simulation methods. This study analyzes QAOA's performance using various quantum simulators (e.g., density_matrix, statevector, and matrix_product_state) and demonstrates the benefits of HPC-QC integrated systems in solving QUBO problems on an active learning workflow. By simulating QAOA on dense, large-matrix QUBO problems, we evaluate accuracy and problem-solving time. We also assess QAOA's performance on local computers and HPC-QC integrated systems, using Oak Ridge Leadership Computing Facility (OLCF)'s Frontier supercomputer with local Qiskit Aer and remote IBM Quantum simulators.

关键词： Quantum approximate optimization algorithm quantum simulator HPC-QC integrated system performance analysis quadratic unconstrained binary optimization

来源：评论

学校读者我要写书评

暂无评论

Quantum Computing for Cable-Routing Problem in Solar Power Plants

Quantum Computing for Cable-Routing Problem in Solar Power P...

引用

North American Power Symposium (NAPS)

作者： Zhao, Zhongqi Fan, Lei Zheng, Honghao Han, Zhu Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Univ Houston Dept Engn Technol Houston TX 77004 USA ComEd Grid Strategy & Analyt Chicago IL USA

ISBN: (纸本)9798350315097

The solar power plant is a large-scale photovoltaic (PV) system aimed to generate solar power for the electricity grid. It includes PV arrays (PVAs), cables, and other electrical accessories. Moreover, the solar power plant builder must consider various parameters and design regulations. The cable routing problem (CRP) is critical in large-scale solar power plant design. The objective of our CRP is to minimize the installation cost of the cable by determining the partition of the photovoltaic array and the cable routing. In this study, we use the quantum computer to solve the CRP, an NP-hard integer linear programming (ILP) problem, and show its advantages over classic computers. We transfer the ILP CRP into the quadratic unconstrained binary optimization (QUBO) model and solve it by the advanced quantum annealer. Finally, we analyze the computational results and discuss the advantages of our approach to solving the CRP.

关键词： Cable Routing Integer Linear Programming optimization quadratic unconstrained binary optimization Quantum Computing Solar Power Plant

来源：评论

学校读者我要写书评

暂无评论

EXPLORING QUANTUM COMPUTING POTENTIALS IN SOLVING A COMBINATORIAL optimization PROBLEM TO MINIMIZE EXPOSURE TO COVID-19 DURING A CITY JOURNEY 24

EXPLORING QUANTUM COMPUTING POTENTIALS IN SOLVING A COMBINAT...

引用

24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Nourbakhsh, A. Jadidi, M. Shahriari, K. York Univ Lassonde Sch Engn Geomat Engn 4700 Keele St Toronto ON M3J 1P3 Canada Intelligent Data Analyt Toronto ON M9A 1R9 Canada

COVID-19 is an airborne virus that can be spread directly or indirectly from one person to another. Spreading the virus strongly depends on the location and time and hence, a Spatio-temporal event. Moreover, traffic congestion will increase the spread of the virus not only because of the vicinity but also because of increased temperature and humidity in these spaces for a short or long time. This paper introduces a vehicle routing optimization model to reduce COVID-19 exposure risk during a city journey by solving it as a quadratic unconstrained binary optimization problem on a quantum annealing computer. Indeed, the objective of the COVID-19 prevention optimization problem is to minimize the risk of exposure for a given set of road users between origins and destinations. Microsoft Taxi data from the city of Beijing have been used to simulate road users' movement. The problem has been run onto three different solvers. One of the solvers is executed on classical computers, and two other solvers are executed on hybrid quantum solvers. Hybrid solvers return the solution within less than 0.03 seconds on quantum processing unit time. However, the results will be returned at least 5 seconds after the execution in the classical solver. It is worth mentioning that, as there is no direct access to the quantum computers, it is hard to compare the results on the same scale as the queries will go on a queue in D-wave quantum computers. Applying the proposed model on the trajectory data shows a better distribution of the vehicles on the road network.

关键词： Vehicle routing problem Quantum computing quadratic unconstrained binary optimization Quantum annealing

来源：评论

学校读者我要写书评

暂无评论

Mapping Constrained Slot-Placement Problems to Ising Models and its Evaluations by an Ising Machine 9

Mapping Constrained Slot-Placement Problems to Ising Models ...

引用

9th IEEE International Conference on Consumer Electronics (ICCE-Berlin)

作者： Kanamaru, Sho Kawamura, Kazushi Tanaka, Shu Tomita, Yoshinori Matsuoka, Hidetoshi Kawamura, Kaoru Togawa, Nozomu Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan Waseda Univ Dept Commun & Comp Engn Tokyo Japan Waseda Univ Green Comp Syst Res Org Tokyo Japan Japan Sci & Technol Agcy Precursory Res Embryon Sci & Technol Tokyo Japan Fujitsu Labs Ltd Kawasaki Kanagawa Japan

ISBN: (纸本)9781728127453

Ising machines have attracted attention, which is expected to obtain better solutions of various combinatorial optimization problems at high speed by mapping the problems to natural phenomena. A slot-placement problem is one of the combinatorial optimization problems, regarded as a quadratic assignment problem, which relates to the optimal logic-block placement in a digital circuit as well as optimal delivery planning. Here, we propose a mapping to the Ising model for solving a slot-placement problem with additional constraints, called a constrained slot-placement problem, where several item pairs must be placed within a given distance. Since the behavior of Ising machines is stochastic, the obtained solution does not always satisfy the slot-placement constraint, which is different from the conventional methods such as the conventional simulated annealing. To resolve the problem, we propose an interpretation method in which a feasible solution is generated by post-processing procedures. Using an Ising machine computer, feasible solutions could be obtained up to 50 times faster than the conventional simulated annealing without degrading accuracy for constrained slot-placement problems with 6 x 6 slots and 27 items at the maximum.

关键词： Ising machine Ising model slot-placement problem quadratic assignment problem quadratic unconstrained binary optimization

来源：评论

学校读者我要写书评

暂无评论

On using QUBO to enforce extensions in abstract argumentation

On using QUBO to enforce extensions in abstract argumentatio...

引用

2023 International Workshop on AI for Quantum and Quantum for AI, AIQxQIA 2023

作者： Baioletti, Marco Santini, Francesco Dipartimento di Matematica e Informatica Università degli Studi di Perugia Perugia Italy

This paper summarizes our first discoveries in encoding Abstract Argumentation problems as quadratic unconstrained binary optimization problems. Classical and quantum annealers can then solve such formulations. In particular, we focus here on an optimization approach to extension enforcement, where a set of arguments is requested to satisfy a given semantics through possible changes of the considered Abstract Argumentation framework. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Argumentation Extension enforcing quadratic unconstrained binary optimization

来源：评论

学校读者我要写书评

暂无评论

Digital Annealing Engine for High-speed Solving of Constrained binary quadratic Problems on Multiple GPUs

Digital Annealing Engine for High-speed Solving of Constrain...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Katayama, Kentaro Yoneoka, Noboru Kanda, Kouichi Tamura, Hirotaka Nakayama, Hiroshi Watanabe, Yasuhiro QUANTUM LABORATORY Fujitsu Limited Kawasaki Japan DXR Laboratory Inc Yokohama Japan

ISBN: (纸本)9798350324136

This paper presents a digital annealing engine capable of high-speed solving the binary quadratic Programming Problem (BQP) with equality and inequality constraints. While many quadratic unconstrained binary optimization (QUBO) solvers have been developed for combinatorial optimization problems, they are specialized for 'unconstrained' BQP and struggle to solve 'constrained' BQP at high speed. To address this issue, we developed a digital annealing engine on GPUs that leverages constraints to narrow the search space and directly evaluates inequality violations during Markov Chain Monte Carlo based searches. We evaluated the performance of the Digital Annealer (DA) system with the developed engine on benchmark problems with one-hot (a type of equality constraint) and inequality constraints. The results showed that the developed DA system achieved superior solving performance compared to the previous generation of DA. It also showed comparable performance to state-of-The-Art dedicated solvers. Our results suggest that this digital annealing engine is a promising solution for high-speed solving constrained BQP. © 2024 IEEE.

关键词： binary quadratic Programming Problem Combinatorial optimization inequality constraint Markov Chain Monte Carlo one-hot constraint quadratic unconstrained binary optimization

来源：评论

学校读者我要写书评

暂无评论

Implementation of spectral methods on Ising machines: toward flow simulations on quantum annealers

引用

FLUID DYNAMICS RESEARCH 2024年第6期56卷 061401-061401页

作者： Takagi, Kenichiro Moriya, Naoki Aoki, Shiori Endo, Katsuhiro Muramatsu, Mayu Fukagata, Koji Keio Univ Dept Mech Engn Yokohama 2238522 Japan Natl Inst Adv Ind Sci & Technol Tsukuba 3058568 Japan

We investigate the possibility and current limitations of flow computations using quantum annealers by solving a fundamental flow problem on Ising machines. As a fundamental problem, we consider the one-dimensional advection-diffusion equation. We formulate it in a form suited to Ising machines (i.e. both classical and quantum annealers), perform extensive numerical tests on a classical annealer, and finally test it on an actual quantum annealer. To make it possible to process with an Ising machine, the problem is formulated as a minimization problem of the residual of the governing equation discretized using either the spectral method or the finite difference method. The resulting system equation is then converted to the quadratic unconstrained binary optimization (QUBO) form through the quantization of variables. It is found in numerical tests using a classical annealer that the spectral method requiring a smaller number of variables has a particular merit over the finite difference method because the accuracy deteriorates with the increase of the number of variables. We also found that the computational error varies depending on the condition number of the coefficient matrix. In addition, we extended it to a two-dimensional problem and confirmed its fundamental applicability. From the numerical test using a quantum annealer, however, it turns out that the computation using a quantum annealer is still challenging due largely to the structural difference from the classical annealer, which leaves a number of issues toward its practical use.

关键词： quantum annealing simulated annealing quadratic unconstrained binary optimization spectral method computational fluid dynamics

来源：评论

学校读者我要写书评

暂无评论

Improving Quantum optimization Algorithms by Constraint Relaxation

引用

APPLIED SCIENCES-BASEL 2024年第18期14卷 8099-8099页

作者： Pecyna, Tomasz Rozycki, Rafal IBCH PAS Poznan Supercomp & Networking Ctr PL-61139 Poznan Poland Poznan Univ Tech Inst Comp Sci Poznan Poland

Quantum optimization is a significant area of quantum computing research with anticipated near-term quantum advantages. Current quantum optimization algorithms, most of which are hybrid variational-Hamiltonian-based algorithms, struggle to present quantum devices due to noise and decoherence. Existing techniques attempt to mitigate these issues through employing different Hamiltonian encodings or Hamiltonian clause pruning, but they often rely on optimistic assumptions rather than a deep analysis of the problem structure. We demonstrate how to formulate the problem Hamiltonian for a quantum approximate optimization algorithm that satisfies all the requirements to correctly describe the considered tactical aircraft deconfliction problem, achieving higher probabilities for finding solutions compared to previous works. Our results indicate that constructing Hamiltonians from an unconventional, quantum-specific perspective with a high degree of entanglement results in a linear instead of exponential number of entanglement gates instead and superior performance compared to standard formulations. Specifically, we achieve a higher probability of finding feasible solutions: finding solutions in nine out of nine instances compared to standard Hamiltonian formulations and quadratic programming formulations known from quantum annealers, which only found solutions in seven out of nine instances. These findings suggest that there is substantial potential for further research in quantum Hamiltonian design and that gate-based approaches may offer superior optimization performance over quantum annealers in the future.

关键词： quantum computing quantum optimization quantum approximate optimization algorithm tactical aircraft deconfliction problem quadratic unconstrained binary optimization Hamiltonian noisy intermediate-scale quantum era

来源：评论

学校读者我要写书评

暂无评论

A hybrid quantum-classical algorithm for mixed-integer optimization in power systems

引用

ELECTRIC POWER SYSTEMS RESEARCH 2024年 235卷

作者： Ellinas, Petros Chevalier, Samuel Chatzivasileiadis, Spyros Tech Univ Denmark DTU Dept Wind & Energy Syst DK-2800 Lyngby Denmark

Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and Optimal Transmission Switching to verifying Neural Networks for power system applications. The main issue of these formulations is the computational complexity of the solution algorithms, as they are considered NP-Hard problems. Quantum computing has been tested as a potential solution towards reducing the computational burden imposed by these problems, providing promising results, motivating the can be used to speedup the solution of MILPs. In this work, we present a general framework for solving power system optimization problems with a Quantum Computer (QC), which leverages mathematical tools and QCs' sampling ability to provide accelerated solutions. Our guiding applications are the optimal transmission switching and the verification of neural networks trained to solve a DC Optimal Power Flow. Specifically, using an accelerated version of Benders Decomposition , we split a given MILP into an Integer Master Problem and a linear Subproblem and solve it through a hybrid "quantum-classical"approach, getting the best of both worlds. We provide 2 use cases, and benchmark the developed framework against other classical and hybrid methodologies, to demonstrate the opportunities and challenges of hybrid quantum-classical algorithms for power system mixed integer optimization problems.

关键词： quadratic unconstrained binary optimization Quantum computing Benders decomposition Optimal transmission switching Neural network verification

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：