Results 61 to 70 of about 1,190 (203)

QUBO Models

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

Clustering-Based Sub-QUBO Extraction for Hybrid QUBO Solvers

Quantum Approximate Optimization Algorithm (QAOA) can be used to solve quadratic unconstrained binary optimization (QUBO) problems. However, the size of the solvable problem is limited by the number of qubits. To leverage noisy intermediate-scale quantum (NISQ) devices to solve large-scale QUBO problems, one possible way is to decompose the full ...
Zhao, Wending, Tang, Gaoxiang
openaire   +2 more sources

Converting of Boolean Expression to Linear Equations, Inequalities and QUBO Penalties for Cryptanalysis [PDF]

, 2022
A. I. Pakhomchik, Vladimir Voloshinov, V. M. Vinokur, G. B. Lesovik   +3 more
openalex   +1 more source

Quantum Machine Learning Applications to Medical Images: A Survey

IET Quantum Communication, Volume 7, Issue 1, January/December 2026.
In this review paper, we provide an outline of quantum neural networks (QNNs), quantum convolution neural networks (QCNNs) and various hybrid models. We also explore human brain‐inspired quantum neuromorphic computing by the quantum spiking neural networks (QSNN).
Mahua Nandy Pal, Debashis De, Dipankar Hazra   +2 more
wiley   +1 more source

Quantum-Soft QUBO Suppression for Accurate Object Detection [PDF]

, 2020
Accepted on ECCV ...
Li, Junde, Ghosh, Swaroop
openaire   +2 more sources

QUBO Formulations for a System of Linear Equations [PDF]

, 2022
Kyungtaek Jun
openalex   +1 more source

Dynamic programming in economics on a quantum annealer

Quantitative Economics, Volume 17, Issue 1, Page 1-37, January 2026.
We introduce novel algorithms for solving dynamic programming problems in economics on a quantum annealer, a specialized quantum computer used for combinatorial optimization. Quantum annealers begin in a superposition of all states and generate candidate global solutions in milliseconds, regardless of problem size.
Jesús Fernández‐Villaverde, Isaiah Hull   +1 more
wiley   +1 more source

Towards arbitrary QUBO optimization: analysis of classical and quantum-activated feedforward neural networks

Machine Learning: Science and Technology
Quadratic unconstrained binary optimization (QUBO) is at the heart of many industries and academic fields such as logistics, supply chain, finance, pharmaceutical science, chemistry, IT, and energy sectors, among others (Bangert 2012 Optimization for ...
Chia-Tso Lai, Carsten Blank, Peter Schmelcher, Rick Mukherjee   +3 more
doaj   +1 more source

Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning

Advanced Quantum Technologies, Volume 8, Issue 12, December 2025.
The use of QML in the realm of nuclear physics at low energy is almost nonexistent. Three examples of the use of quantum computing and quantum machine in nuclear physics are presented: the determination of the phase/shape in nuclear models, the calculation of the ground state energy, and the identification of particles in nuclear physics experiments ...
José‐Enrique García‐Ramos, Álvaro Sáiz, José M. Arias, Lucas Lamata, Pedro Pérez‐Fernández   +4 more
wiley   +1 more source

QUBO Modeling for a Spectrum of Domination Problems

Journal of Mathematics
As quantum computing advances, the application of quantum algorithms to address combinatorial optimization problems on quantum computers has emerged as a pivotal area of research.
Haoqian Pan, Changhong Lu
doaj   +1 more source