Results 41 to 50 of about 1,548 (65)
Nanophotonics, Volume 14, Issue 23, Page 3761-3793, 02 November 2025.Abstract Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing.
Yuheng Chen +18 more
wiley +1 more source
Leveraging Quantum Annealing for Layout Optimization
Advanced Quantum Technologies, Volume 8, Issue 11, November 2025.The authors address wind farm layout optimization by formulating it as a QUBO problem using the Jensen wake model. They compare quantum annealing, Gurobi, and QAOA, highlighting trade‐offs between solution quality and computational time. Results show that quantum annealing offers rapid, near‐optimal solutions, making it suitable for fast approximations
Luca Nigro +3 more
wiley +1 more source
Track clustering with a quantum annealer for primary vertex reconstruction at hadron colliders
, 2020 Clustering of charged particle tracks along the beam axis is the first step in reconstructing the positions of hadronic interactions, also known as primary vertices, at hadron collider experiments.
Das, Souvik +3 more
core
Most Frequent Itemset Optimization
, 2019 In this paper we are dealing with the frequent itemset mining. We concentrate on the special case that we only want to identify the most frequent itemset of length N.
Nüßlein, Jonas
core
Some of the next articles are maybe not open access.
Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO)
Journal of Heuristics, 2007We present a family of local-search-based heuristics for Quadratic Unconstrained Binary Optimization (QUBO), all of which start with a (possibly fractional) initial point, sequentially improving its quality by rounding or switching the value of one variable, until arriving to a local optimum. The effects of various parameters on the efficiency of these
Endre Boros, Peter L Hammer
exaly +2 more sources
Quadratic Unconstrained Binary Optimization (QUBO) on neuromorphic computing system
2017 International Joint Conference on Neural Networks (IJCNN), 2017The problems of Artificial intelligence (AI) naturally maps to NP-hard optimization problems. This trend has significance to achieve human-level computation capability from machines. This computational ability can be achieved by developing evolutionary algorithms or mapping those evolutionary algorithms onto new generation computing systems: Quantum or
Zahangir Alom, Tarek M Taha
exaly +2 more sources
Proceedings of the International Conference on Neuromorphic Systems, 2019
In this work, graph partitioning (GP) is explored using quadratic unconstrained binary optimization (QUBO) on the IBM TrueNorth spiking neuromorphic architecture. GP splits a graph into similar-sized parts while minimizing the number of cut edges between parts. Classical approaches to GP rely on heuristics and approximation algorithms.
Susan M Mniszewski
exaly +2 more sources
In this work, graph partitioning (GP) is explored using quadratic unconstrained binary optimization (QUBO) on the IBM TrueNorth spiking neuromorphic architecture. GP splits a graph into similar-sized parts while minimizing the number of cut edges between parts. Classical approaches to GP rely on heuristics and approximation algorithms.
Susan M Mniszewski
exaly +2 more sources
2008
This dissertation investigates the Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e. the problem of minimizing a quadratic function in binary variables. QUBO is studied at two complementary levels. First, there is an algorithmic aspect that tells how to preprocess the problem, how to find heuristics, how to get improved bounds and how to
openaire +1 more source
This dissertation investigates the Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e. the problem of minimizing a quadratic function in binary variables. QUBO is studied at two complementary levels. First, there is an algorithmic aspect that tells how to preprocess the problem, how to find heuristics, how to get improved bounds and how to
openaire +1 more source