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 need...
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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 quantumalgorithm (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.
The effects of noise on NISQ devices strongly limit the current applicability of quantum combinatorial optimizationalgorithms. In this work, we integrate ZNE, an error mitigation technique, into the QAOA, thus adding...
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ISBN:
(纸本)9798331541378
The effects of noise on NISQ devices strongly limit the current applicability of quantum combinatorial optimizationalgorithms. In this work, we integrate ZNE, an error mitigation technique, into the QAOA, thus adding further reliability to the method. We also perform a benchmark to assess the effects of mitigation on the exploration of the parameters space performed by the optimizer.
With the continuous development of network technology and hardware devices, panoramic livecast is promising and widely used in various industries. To meet massive heterogeneous viewer demands, livecast video streams m...
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ISBN:
(纸本)9798350303582;9798350303599
With the continuous development of network technology and hardware devices, panoramic livecast is promising and widely used in various industries. To meet massive heterogeneous viewer demands, livecast video streams must be transcoded into multiple versions and then transmitted to viewers. By offloading transcoding workloads to network nodes closer to broadcasters and viewers, computing power networks (CPN) have been considered an effective means to provide viewers with a higher quality of experience (QoE). In this paper, we study the joint video transmission and transcoding resource allocation in the CPN-based panoramic livecast system. Considering the versatility and simplicity, we offer a multi-layer network model to capture the stochastic characteristics of transmission and transcoding processes and transform the joint resource allocation problem into a broader shortest path problem (SPP). As the scale of networks expands, the SPP may become intractable on classical computers. This paper explores the viability of solving the SPP on quantum computers by utilizing quantum resources such as superposition and entanglement, then proposes a shortest path algorithm based on the quantum approximate optimization algorithm (QAOA-SPP) for jointly optimizing the latency and overhead of transmission and transcoding. Simulation results indicate that our algorithm can achieve better performance than the benchmark schemes under reasonable parameter settings.
This paper introduces an approach to solve unit commitment-based DC optimal power flow using the quantum-based alternating direction method of multipliers optimizer. The substantial interconnection of current generati...
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ISBN:
(纸本)9798331521042;9798331521035
This paper introduces an approach to solve unit commitment-based DC optimal power flow using the quantum-based alternating direction method of multipliers optimizer. The substantial interconnection of current generation and loads and the widespread incorporation of renewable energy have significantly altered and increased the complexity of the grid. As a result, efficient planning and operation of the power grid have become central focuses. This paper proposes an advanced method to solve DC-optimal power flow via quantum heuristic approaches leveraging the power of both quantum and classical computing. This is achieved by splitting the procedure to decompose a mixed binary quadratic problem into a quadratic unconstrained binary optimization sub-problem which is solved using a quantum approximate optimization algorithm. Following this, a continuous convex-constrained sub-problem is solved with a classical optimization solver. Within the DC-OPF, the startup cost and shutdown cost of each generator are incorporated in addition to the general cost function with the generator's cost coefficients. The DC-OPF is solved using the IBM Qiskit software development kit on an IEEE-14 bus system. The results obtained are then validated with the results obtained from classical methods using MATPOWER.
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, head...
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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 quantumalgorithms 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.
The quantum approximate optimization algorithm (QAOA) is one of the most promising Noisy Intermediate quantum (NISQ) algorithms in solving combinatorial optimizations and displays potential over classical heuristic te...
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The quantum approximate optimization algorithm (QAOA) is one of the most promising Noisy Intermediate quantum (NISQ) algorithms in solving combinatorial optimizations and displays potential over classical heuristic techniques. Unfortunately, QAOA's performance depends on the choice of parameters and standard optimizers often fail to identify key parameters due to the complexity and mystery of these optimization functions. In this paper, we benchmark QAOA circuits modified with metaheuristic optimizers against classical and quantum heuristics to identify QAOA parameters. The experimental results reveal insights into the strengths and limitations of both quantum Annealing and metaheuristic-integrated QAOA across different problem domains. The findings suggest that the hybrid approach can leverage classical optimization strategies to enhance the solution quality and convergence speed of QAOA, particularly for problems with rugged landscapes and limited quantum resources. Furthermore, the study provides guidelines for selecting the most appropriate approach based on the specific characteristics of the optimization problem at hand.
The quantum approximate optimization algorithm (QAOA) is a promising candidate for solving combinatorial optimization problems more efficiently than classical computers. Recent studies have shown that warm-starting th...
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ISBN:
(纸本)9781510670839;9781510670822
The quantum approximate optimization algorithm (QAOA) is a promising candidate for solving combinatorial optimization problems more efficiently than classical computers. Recent studies have shown that warm-starting the standard algorithm improves the performance. In this paper we compare the performance of standard QAOA with that of warm-start QAOA in the context of portfolio optimization and investigate the warm-start approach for different problem instances. In particular, we analyze the extent to which the improved performance of warm-start QAOA is due to quantum effects, and show that the results can be reproduced or even surpassed by a purely classical preprocessing of the original problem followed by standard QAOA.
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...
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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 quantumoptimization 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).
This paper introduces HamilToniQ, an open -source benchmarking toolkit for quantum Processing Units (QPUs). It addresses the complexities of quantum computations by providing a methodological framework to assess QPU t...
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ISBN:
(纸本)9798331541378
This paper introduces HamilToniQ, an open -source benchmarking toolkit for quantum Processing Units (QPUs). It addresses the complexities of quantum computations by providing a methodological framework to assess QPU types, topologies, and systems. HamilToniQ facilitates performance evaluations through steps like circuit compilation and quantum error mitigation, with strategies tailored for each stage. The toolkit's H -Score measures QPU fidelity and reliability, offering a comprehensive view of performance. Focused on the quantum approximate optimization algorithm (QAOA), HamilToniQ enables consistent QPU comparisons, enhancing benchmarking transparency. Validated on various IBM QPUs, the toolkit proves effective and robust, advancing quantum computing with precise benchmarking metrics.
The quantum approximate optimization algorithm (QAOA) has become one of the most widely used components in the development of modern quantum applications. It works on the paradigm of quantum variational circuits, wher...
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ISBN:
(纸本)9798350332285
The quantum approximate optimization algorithm (QAOA) has become one of the most widely used components in the development of modern quantum applications. It works on the paradigm of quantum variational circuits, where a quantum circuit is trained - by repeatedly adjusting circuit parameters - to adequately solve a combinatorial optimization problem. This training process, based on classical optimizationalgorithms, represents a significant computational bottleneck for QAOA, as it requires repeated calls to the quantum device to evaluate the cost function of the problem to solve. Therefore, there is a strong need to eliminate this computationally expensive task and identify an alternative strategy to compute good parameters for QAOA. This paper synergistically exploits the parameter concentration property of QAOA and the Fuzzy C-Means algorithm to achieve this goal. Experimental results show that the proposed approach can support QAOA to maintain high performance in solving well-known optimization problems such as MAXCUT, requiring a reduced computational effort for the parameter tuning phase.
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