Results 11 to 20 of about 1,548 (65)
Towards Prediction of Financial Crashes with a D-Wave Quantum Computer [PDF]
Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, i.e., a problem which cannot be solved efficiently with a classical computer.
Enrique Lizaso +9 more
core +2 more sources
Prime factorization using quantum annealing and computational algebraic geometry
We investigate prime factorization from two perspectives: quantum annealing and computational algebraic geometry, specifically Gr\"obner bases. We present a novel scalable algorithm which combines the two approaches and leads to the factorization of all ...
Alghassi, Hedayat, Dridi, Raouf
core +1 more source
Consistently Orienting Facets in Polygon Meshes by Minimizing the Dirichlet Energy of Generalized Winding Numbers [PDF]
Jacobson et al. [JKSH13] hypothesized that the local coherency of the generalized winding number function could be used to correctly determine consistent facet orientations in polygon meshes.
Jacobson, Alec +3 more
core +3 more sources
An Integrated Programming and Development Environment for Adiabatic Quantum Optimization
Adiabatic quantum computing is a promising route to the computational power afforded by quantum information processing. The recent availability of adiabatic hardware has raised challenging questions about how to evaluate adiabatic quantum optimization ...
Bennink, Ryan S. +7 more
core +1 more source
Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer [PDF]
The D-Wave adiabatic quantum annealer solves hard combinatorial optimization problems leveraging quantum physics. The newest version features over 1000 qubits and was released in August 2015.
Koch, Christoph, Trummer, Immanuel
core +2 more sources
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley +1 more source
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
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
Quantum-enhanced reinforcement learning for finite-episode games with discrete state spaces
Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems, have been ...
Compostella, Gabriele +3 more
core +2 more sources
Establishing Shape Correspondences: A Survey
Abstract Shape correspondence between surfaces in 3D is a central problem in geometry processing, concerned with establishing meaningful relations between surfaces. While all correspondence problems share this goal, specific formulations can differ significantly: Downstream applications require certain properties that correspondences must satisfy ...
A. Heuschling, H. Meinhold, L. Kobbelt
wiley +1 more source

