On the QUBO formulation of the Hamiltonian cycle problem
Abstract Combinatorial problems pose significant challenges, especially for large-scale instances. A promising approach to addressing these challenges involves quadratic unconstrained binary optimization (QUBO) models, a key framework in quantum computing.
Durbec, Amelia +2 more
openaire +3 more sources
A QUBO Formulation for Multi‐Material Topology Optimization With Quantum Annealing
ABSTRACT Multi‐material topology optimization (MMTO) seeks the optimal distribution of multiple materials within a given design domain to maximize structural performance. However, due to the high complexity of the solution space, MMTO problems are often trapped in local optima. To address this issue, a quantum annealing‐based MMTO framework is proposed.
Kenshin Nakano +2 more
wiley +1 more source
Computing the Molecular Ground State Energy in a Restricted Active Space Using Quantum Annealing
Can modern quantum annealers tackle realistic molecular problems? By combining XBK Hamiltonian mappings, next‐generation D‐Wave hardware, and advanced annealing protocols, significantly larger molecular instances become accessible with improved energy accuracy.
Stefano Bruni, Enrico Prati
wiley +1 more source
Utilizing Graph Sparsification for Pre-processing in Maxcut QUBO Solver [PDF]
We suggest employing graph sparsification as a pre-processing step for maxcut programs using the QUBO solver. Quantum(-inspired) algorithms are recognized for their potential efficiency in handling quadratic unconstrained binary optimization (QUBO ...
Hao, Jin-Kao, Suppakitpaisarn, Vorapong
core +1 more source
Abstract Having previously studied general aspects of QUBO models, we now study application specific examples and how to derive or set them up. Our application examples will consider problems in areas like combinatorial optimization, constraint satisfaction, data mining, and machine learning and are supposed to illustrate that QUBOs ...
Christian Bauckhage, Rafet Sifa
openaire +1 more source
Toward Solution‐Time Advantage With Error‐Mitigated Quantum Annealing for Combinatorial Optimization
This paper presents a novel error mitigation technique to address the qubit errors that occur when solving combinatorial optimization problems with quantum annealing. The approach significantly speeds up the computation to reach the global optimum solution for a correlated 3D image segmentation model for material microstructures, demonstrating a ...
Yushuang Sam Yang +3 more
wiley +1 more source
Discrete quadratic model QUBO solution landscapes [PDF]
Many computational problems involve optimization over discrete variables with quadratic interactions. Known as discrete quadratic models (DQMs), these problems in general are NP-hard.
Zaborniak, Tristan, Stege, Ulrike
core +1 more source
Modularity‐Preserving Hamiltonian Compression for QAOA‐Based Community Detection
This work explores several quantum methods to community detection in networks by simplifying modularity‐based Hamiltonians. By reducing circuit complexity through sparsification, spectral projection, and penalty techniques, the methods retain key structural information while improving efficiency.
Danilo Cavaliere +2 more
wiley +1 more source
QUBO-tuning for particle track reconstruction - Optimisation strategies and parameter settings
Quadratic Unconstraint Binary Optimization (QUBO) offers an exceptional approach to tackle particle track reconstruction - to encode the data into a single equation and solve it as a whole.To minimise the QUBO cost function, a quantum device can be ...
Funcke, Lena +10 more
core
Solving Standard and Generalized EMPM Eigenvalue Problems: A QUBO Approach for the D-Wave Quantum Annealer [PDF]
Within the Equation of Motion Phonon Method (EMPM) framework, we address the computation of the ground-state eigenpair of nuclear Hamiltonians by reformulating the eigenvalue problem as a Quadratic Unconstrained Binary Optimization (QUBO).
De Gregorio G. +8 more
doaj +1 more source

