On the emerging potential of quantum annealing hardware for combinatorial optimization
Over the past decade, the usefulness of quantum annealing hardware for combinatorial
optimization has been the subject of much debate. Thus far, experimental benchmarking …
optimization has been the subject of much debate. Thus far, experimental benchmarking …
Solving the resource constrained project scheduling problem with quantum annealing
Quantum annealing emerges as a promising approach for tackling complex scheduling
problems such as the resource-constrained project scheduling problem (RCPSP). This study …
problems such as the resource-constrained project scheduling problem (RCPSP). This study …
Embedding of complete graphs in broken Chimera graphs
E Lobe, L Schürmann, T Stollenwerk - Quantum Information Processing, 2021 - Springer
In order to solve real-world combinatorial optimization problems with a D-Wave quantum
annealer, it is necessary to embed the problem at hand into the D-Wave hardware graph …
annealer, it is necessary to embed the problem at hand into the D-Wave hardware graph …
Integer programming techniques for minor-embedding in quantum annealers
A major limitation of current generations of quantum annealers is the sparse connectivity of
manufactured qubits in the hardware graph. This technological limitation has generated …
manufactured qubits in the hardware graph. This technological limitation has generated …
Minor embedding in broken Chimera and Pegasus graphs is NP-complete
E Lobe, A Lutz - arXiv preprint arXiv:2110.08325, 2021 - arxiv.org
The embedding is an essential step when calculating on the D-Wave machine. In this work
we show the hardness of the embedding problem for both types of existing hardware …
we show the hardness of the embedding problem for both types of existing hardware …
[HTML][HTML] Minor embedding in broken chimera and derived graphs is np-complete
E Lobe, A Lutz - Theoretical Computer Science, 2024 - Elsevier
The embedding is an essential step when calculating on the D-Wave machine. In this work,
we show the hardness of the embedding problem for all types of currently existing hardware …
we show the hardness of the embedding problem for all types of currently existing hardware …
FlexSpin: A CMOS Ising Machine With 256 Flexible Spin Processing Elements With 8-b Coefficients for Solving Combinatorial Optimization Problems
Combinatorial optimization problems (COPs) are essential in various applications, including
data clustering, supply chain management, and communication networks. Many real-world …
data clustering, supply chain management, and communication networks. Many real-world …
Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing
LF Pérez Armas, S Creemers… - Available at SSRN …, 2024 - papers.ssrn.com
Quantum annealing emerges as a viable solution for solving complex problems such as the
resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer …
resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer …
CHARME: A chain-based reinforcement learning approach for the minor embedding problem
Quantum Annealing (QA) holds great potential for solving combinatorial optimization
problems efficiently. However, the effectiveness of QA algorithms heavily relies on the …
problems efficiently. However, the effectiveness of QA algorithms heavily relies on the …
Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing
LFP Armas, S Creemers, S Deleplanque - 2024 - hal.science
Quantum annealing emerges as a viable solution for solving complex problems such as the
resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer …
resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer …