Results 51 to 60 of about 18,556 (186)

Speedup of high-order unconstrained binary optimization using quantum $${{\mathbb{Z}}}_{2}$$ Z 2 lattice gauge theory

Communications Physics
An important and difficult problem in optimization is the high-order unconstrained binary optimization, which can represent many optimization problems more efficiently than quadratic unconstrained binary optimization, but how to quickly solve it has ...
Bi-Ying Wang, Xiaopeng Cui, Qingguo Zeng, Yemin Zhan, Man-Hong Yung, Yu Shi   +5 more
doaj   +1 more source

Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

, 2010
Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio.
A Fernández, A Meucci, A Schaerf, A Scozzari, AD Roy, Andrea Scozzari, C Acerbi, C Acerbi, CI Fábián, D Bertsimas, D Bertsimas, D Bienstock, D Lorenzo Di, D Maringer, DX Shawa, E Angelelli, EJ Elton, F Cesarone, F Cesarone, F Tardella, F Tardella, Fabio Tardella, Francesco Cesarone, G Mitra, H Kellerer, H Konno, H Konno, H Konno, HM Markowitz, IM Bomze, JE Beasley, KP Anagnostopoulos, L Chiodi, L Gaspero Di, MG Speranza, MG Speranza, NA Canakgoz, NJ Jobst, P Artzner, R Bruni, R Bruni, R Mansini, R Mansini, R Mansini, RT Rockafellar, RT Rockafellar, ST Rachev, TJ Chang   +47 more
core   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
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

Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization [PDF]

, 2008
Many artificial intelligence (AI) problems naturally map to NP-hard optimization problems. This has the interesting consequence that enabling human-level capability in machines often requires systems that can handle formally intractable problems.
Macready, William G., Neven, Hartmut, Rose, Geordie   +2 more
core  

An Integrated Programming and Development Environment for Adiabatic Quantum Optimization

, 2014
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., Billings, Jay J., D'Azevedo, Ed F., Humble, Travis S., Klymko, Christine F., McCaskey, Alex J., Seddiqi, Hadayat, Sullivan, Blair D.   +7 more
core   +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

Solving Standard and Generalized EMPM Eigenvalue Problems: A QUBO Approach for the D-Wave Quantum Annealer [PDF]

EPJ Web of Conferences
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., De Lucia C., Martone A., D’Aniello F.A., Mastroianni A., Nunziata G., Knapp F., Iudice N. Lo, Veselý P.   +8 more
doaj   +1 more source

Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction

Advanced Intelligent Systems, EarlyView.
This study proposes time‐delayed spiking reservoir computing (TDSRC) for efficient time series prediction. By concatenating time‐lagged states, TDSRC constructs an expanded readout feature vector without altering internal reservoir dynamics. This approach enables highly accurate forecasting with significantly fewer neurons, providing a resource ...
Pin Jin, Ren Cao, Zhongxue Gan, Siyang Leng   +3 more
wiley   +1 more source

Embedding Equality Constraints of Optimization Problems into a Quantum Annealer

Algorithms, 2019
Quantum annealers such as D-Wave machines are designed to propose solutions for quadratic unconstrained binary optimization (QUBO) problems by mapping them onto the quantum processing unit, which tries to find a solution by measuring the parameters of a ...
Tomas Vyskocil, Hristo Djidjev
doaj   +1 more source

Toward Solution‐Time Advantage With Error‐Mitigated Quantum Annealing for Combinatorial Optimization

Advanced Physics Research, EarlyView.
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, Peter Tyson, Clement Chu, Anthony Bruce Murphy   +3 more
wiley   +1 more source