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- research-articleAugust 2024
Optimization Applications as Quantum Performance Benchmarks
- Thomas Lubinski,
- Carleton Coffrin,
- Catherine McGeoch,
- Pratik Sathe,
- Joshua Apanavicius,
- David Bernal Neira,
- Quantum Economic Development Consortium(QED-C) Collaboration
ACM Transactions on Quantum Computing (TQC), Volume 5, Issue 3Article No.: 18, Pages 1–44https://doi.org/10.1145/3678184Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm and Quantum Annealing can potentially demonstrate significant run-time performance ...
- ArticleJuly 2024
Solving Maximum Cut Problem with Multi-objective Enhance Quantum Approximate Optimization Algorithm
Computational Science and Its Applications – ICCSA 2024 WorkshopsPages 244–252https://doi.org/10.1007/978-3-031-65343-8_16AbstractThis article presents a novel approach to enhancing the performance of the Quantum Approximate Optimization Algorithm (QAOA), a method used to tackle combinatorial optimization problems. However, it has many disadvantages because of classical ...
- ArticleJune 2024
Quantum Optimization Approach for Feature Selection in Machine Learning
AbstractThis is intended to be a technical companion presenting some achievements recently published about the usage of quantum algorithms for the selection of relevant features in a given data set. Based on the paradigm of machine learning, such methods ...
- ArticleJune 2024
Solving Quadratic Knapsack Problem with Biased Quantum State Optimization Algorithm
AbstractThe Quantum Approximate Optimization Algorithm is the hybrid classic-quantum algorithm that is used for solving the combinatorial optimization problem. However, the algorithm performs poorly in the constrained combinatorial optimization problem ...
- ArticleJune 2024
Solving Edge-Weighted Maximum Clique Problem with DCA Warm-Start Quantum Approximate Optimization Algorithm
AbstractThe Quantum Approximate Optimization Algorithm is a hybrid quantum-classic algorithm used for solving combinatorial optimization. However, this algorithm performs poorly when solving the constrained combinatorial optimization problem. To deal with ...
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- ArticleJune 2024
Indirect Flow-Shop Coding Using Rank: Application to Indirect QAOA
AbstractThe Flow-Shop Scheduling Problem (FSSP) is one of the most famous scheduling problems. The Flow-Shop scheduling problem is a disjunctive problem, meaning that a solution is fully described by an oriented disjunctive graph where the earliest ...
- research-articleAugust 2024
Quantum Solution for Configuration Selection and Prioritization
Q-SE 2024: Proceedings of the 5th ACM/IEEE International Workshop on Quantum Software EngineeringPages 21–28https://doi.org/10.1145/3643667.3648221The analyses of highly configurable systems, as applied in software or automotive domains, yield hard problems due to the exponentially increasing number of possible product configurations. Current research identified that such combinatorial optimization ...
- research-articleJuly 2024
Transfer of Logistics Optimizations to Material Flow Resource Optimizations using Quantum Computing
Procedia Computer Science (PROCS), Volume 232, Issue CPages 32–42https://doi.org/10.1016/j.procs.2024.01.004AbstractThe complexity of industrial logistics and manufacturing processes increases constantly. As a key enabling technology of the upcoming decades, quantum computing is expected to play a crucial role in solving arising combinatorial optimization ...
- research-articleDecember 2023
Improve the Quantum Approximate Optimization Algorithm with Genetic Algorithm
SOICT '23: Proceedings of the 12th International Symposium on Information and Communication TechnologyPages 655–662https://doi.org/10.1145/3628797.3628818The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum optimization technique used for solving combinatorial optimization problems. However, in constrained binary optimization, QAOA’s reliance on equal initial probabilities for ...
- research-articleJanuary 2024
QAOA-based MRMR Algorithm for Feature Selection
AAIA '23: Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and ApplicationsPages 277–282https://doi.org/10.1145/3603273.3631193As the volume of data for classification tasks in machine learning grows, feature selection plays an increasingly crucial role in enhancing the efficiency and effectiveness of these tasks. Existing classical feature selection algorithms often encounter ...
- research-articleJuly 2023
Solving Scheduling Problems with Quantum Computing: a Study on Flexible Open Shop
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationPages 2175–2178https://doi.org/10.1145/3583133.3596420Despite quantum computing is revealing an increasingly promising technology that has the potential to introduce a significant speed-up in many areas of computation, the number of problems that it can represent and solve is currently rather limited. ...
- ArticleJuly 2023
Solving Higher Order Binary Optimization Problems on NISQ Devices: Experiments and Limitations
AbstractWith the recent availability of Noisy Intermediate-Scale Quantum devices, the potential of quantum computers to impact the field of combinatorial optimization lies in quantum variational and annealing-based methods. This paper further compares ...
- ArticleJuly 2023
TAQOS: A Benchmark Protocol for Quantum Optimization Systems
AbstractThe growing availability of quantum computers raises questions about their ability to solve concrete problems. Existing benchmark protocols still lack problem diversity and attempt to summarize quantum advantage in a single metric that measures ...
- ArticleJuly 2023
Software Aided Approach for Constrained Optimization Based on QAOA Modifications
AbstractWe present two variants of the QAOA modification for solving constrained combinatorial problems. The results presented in this paper were obtained using the QHyper framework, which we developed specifically for this purpose. More specifically, we ...
- research-articleJune 2023
Constructing Optimal Bushy Join Trees by Solving QUBO Problems on Quantum Hardware and Simulators
BiDEDE '23: Proceedings of the International Workshop on Big Data in Emergent Distributed EnvironmentsArticle No.: 7, Pages 1–7https://doi.org/10.1145/3579142.3594298The join order is one of the most important factors that impact the speed of query processing. Its optimization is known to be NP-hard, such that it is worth investigating the benefits of utilizing quantum computers for optimizing join orders. Hence ...
- research-articleApril 2023
Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
ACM Transactions on Quantum Computing (TQC), Volume 4, Issue 3Article No.: 19, Pages 1–15https://doi.org/10.1145/3584706Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unweighted MaxCut problems by leveraging similarities in the objective landscape ...
- research-articleFebruary 2024
Exploiting the Regular Structure of Modern Quantum Architectures for Compiling and Optimizing Programs with Permutable Operators
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4Pages 108–124https://doi.org/10.1145/3623278.3624751A critical feature in today's quantum circuit is that they have permutable two-qubit operators. The flexibility in ordering the permutable two-qubit gates leads to more compiler optimization opportunities. However, it also imposes significant challenges ...
- research-articleFebruary 2023
Bridging Classical and Quantum with SDP initialized warm-starts for QAOA
ACM Transactions on Quantum Computing (TQC), Volume 4, Issue 2Article No.: 9, Pages 1–39https://doi.org/10.1145/3549554We study the Quantum Approximate Optimization Algorithm (QAOA) in the context of the Max-Cut problem. Noisy quantum devices are only able to accurately execute QAOA at low circuit depths, while classically-challenging problem instances may call for a ...
- research-articleJanuary 2023
Approaches to Constrained Quantum Approximate Optimization
AbstractWe study the costs and benefits of different quantum approaches to finding approximate solutions of constrained combinatorial optimization problems with a focus on the maximum independent set. Using the Lagrange multiplier approach, we analyze the ...