Results 31 to 40 of about 1,254,139 (165)

Benchmarking quantum annealing with maximum cardinality matching problems

Frontiers in Computer Science
We benchmark Quantum Annealing (QA) vs. Simulated Annealing (SA) with a focus on the impact of the embedding of problems onto the different topologies of the D-Wave quantum annealers.
Daniel Vert, Daniel Vert, Madita Willsch, Madita Willsch, Berat Yenilen, Berat Yenilen, Renaud Sirdey, Renaud Sirdey, Stéphane Louise, Stéphane Louise, Kristel Michielsen, Kristel Michielsen, Kristel Michielsen   +12 more
doaj   +1 more source

Application of quantum computing techniques in particle tracking at LHC [PDF]

EPJ Web of Conferences
After the next planned upgrades to the LHC, the luminosity it delivers will more than double, substantially increasing the already large demand on computing resources. Therefore an efficient way to reconstruct physical objects is required. Recent studies
Chan Wai Yuen, Akiyama Daiya, Arakawa Koki, Ganguly Sanmay, Kaji Toshiaki, Minami Juri, Sawada Ryu, Tanaka Junichi, Terashi Koji, Yorita Kohei   +9 more
doaj   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Quantum annealing speedup over simulated annealing on random Ising chains

, 2016
We show clear evidence of a quadratic speedup of a quantum annealing (QA) Schrödinger dynamics over a Glauber master equation simulated annealing (SA) for a random Ising model in one dimension, via an equal-footing exact deterministic dynamics of the ...
Zanca, Tommaso, Santoro, Giuseppe E.
core   +1 more source

GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm [PDF]

, 2022
We study large-scale applications using a GPU-accelerated version of the massively parallel J\"ulich universal quantum computer simulator (JUQCS--G). First, we benchmark JUWELS Booster, a GPU cluster with 3744 NVIDIA A100 Tensor Core GPUs.
Jin, Fengping, Willsch, Madita, Willsch, Dennis, De Raedt, Hans, Michielsen, Kristel   +4 more
core   +2 more sources

A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design

Advanced Intelligent Discovery, EarlyView.
Active learning (AL) strategies are benchmarked for the binary design of planar multilayer nanophotonic structures. Factorization machines combined with quantum annealing (QA) become effective as dimensionality increases. Hybrid QA provides the strongest results for 100‐layer problems, highlighting the importance of optimization method selection in ...
Serang Jung, Seongmin Kim, Jaehyun Park, Chaeyoung Park, Zhihao Xu, Sanghyo Hwang, Baiqing Ye, Youn sang Kim, Sun‐Kyung Kim, Tengfei Luo, Eungkyu Lee   +10 more
wiley   +1 more source

Superconducting qubits for quantum annealing applications [PDF]

, 2022
Over the last two decades, Quantum Annealing (QA) has grown to be a commercial technology with machines reaching the scale of 5000 interconnected qubits.
Consani, Gioele
core   +1 more source

Quantum annealing of a hard combinatorial problem [PDF]

, 2011
Projecte Final de Màster Oficial fet en col.laboració amb el Departament de Física Fonamental, Facultat de Física,Universitat de BarcelonaWe present the numerical results obtained using quantum annealing (QA) in a hard combinatorial problem: the ...
Lecina Casas, Daniel
core   +2 more sources

Analog and digital adiabatic quantum annealing with oscillating transverse fields

, 2022
Includes bibliographical references.2021 Fall.This thesis investigates both analog Quantum Annealing and digital Quantum Annealing with oscillating transverse field in solving hard optimization problems.
Tang, Zhijie
core  

Development of Hamiltonian for Structural Applications by Quantum Annealing Assuming Finite Element Method

International Journal for Numerical Methods in Engineering, Volume 127, Issue 17, 15 September 2026.
ABSTRACT Quantum annealing is emerging as a practical tool for large‐scale combinatorial optimization. We map continuum displacements and element densities to binary variables, turning both deformation analysis and topology optimization into quadratic unconstrained binary optimization (QUBO) problems that run on today's annealers.
Rio Honda, Katsuhiro Endo, Yudai Suzuki, Yoshiki Matsuda, Shu Tanaka, Mayu Muramatsu   +5 more
wiley   +1 more source