Results 51 to 60 of about 3,959 (191)

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

For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances [PDF]

, 2018
The Quantum Approximate Optimization Algorithm, QAOA, uses a shallow depth quantum circuit to produce a parameter dependent state. For a given combinatorial optimization problem instance, the quantum expectation of the associated cost function is the ...
Brandao, Fernando G. S. L., Broughton, Michael, Farhi, Edward, Gutmann, Sam, Neven, Hartmut   +4 more
core   +1 more source

A Resource Efficient Ising Model‐Based Quantum Sudoku Solver

Software: Practice and Experience, Volume 56, Issue 6, Page 643-657, June 2026.
ABSTRACT Background Quantum algorithms exploit superposition and parallelism to address complex combinatorial problems, many of which fall into the non‐polynomial (NP) class. Sudoku, a widely known logic‐based puzzle, is proven to be NP‐complete and thus presents a suitable testbed for exploring quantum optimization approaches.
Wen‐Li Wang, Mei‐Huei Tang, Kevin Wang, Muhammad Abdul Basit, Yuan Tian, Md. Sanaul Haque   +5 more
wiley   +1 more source

Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation [PDF]

Quantum
The Quantum Approximate Optimisation Algorithm (QAOA) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While QAOA targets problems in complexity class NP, the classical optimisation procedure required in every ...
Tom Krüger, Wolfgang Mauerer
doaj   +1 more source

End‐to‐End Portfolio Optimization with Hybrid Quantum Annealing

Advanced Quantum Technologies, Volume 9, Issue 4, April 2026.
This works presents a hybrid quantum‐classical framework for portfolio optimization that combines quantum assisted asset selection and rebalancing with classical weight allocation. The approach processes real market data, embeds it into Quadratic Unconstrained Binary Optimization formulations, and evaluates performance within a unified workflow ...
Sai Nandan Morapakula, Sangram Deshpande, Rakesh Yata, Rushikesh Ubale, Uday Wad, Kazuki Ikeda   +5 more
wiley   +1 more source

Scaling quantum approximate optimization on near-term hardware

Scientific Reports, 2022
The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial optimization problems.
Phillip C. Lotshaw, Thien Nguyen, Anthony Santana, Alexander McCaskey, Rebekah Herrman, James Ostrowski, George Siopsis, Travis S. Humble   +7 more
doaj   +1 more source

Qubit‐Efficient Quantum Local Search for Combinatorial Optimization

Advanced Quantum Technologies, Volume 9, Issue 3, March 2026.
We introduce a qubit‐efficient variational quantum algorithm for combinatorial optimization that adaptively uses from logarithmic to a linear number of qubits to implement quantum local search. The method encodes flip probabilities of spin groups into quantum amplitudes, enabling exploration of classically intractable neighborhoods while maintaining ...
Mikhail Podobrii, Viacheslav Kuzmin, Vladimir Voloshinov, Margarita Veshchezerova, Michael R. Perelshtein   +4 more
wiley   +1 more source

Restricted global optimization for QAOA

APL Quantum
The Quantum Approximate Optimization Algorithm (QAOA) has emerged as a promising variational quantum algorithm for addressing NP-hard combinatorial optimization problems. However, a significant limitation lies in optimizing its classical parameters, which is in itself an NP-hard problem.
Peter Gleißner, Georg Kruse, Andreas Roßkopf   +2 more
openaire   +3 more sources

Addressing ecological challenges from a quantum computing perspective

Methods in Ecology and Evolution, Volume 17, Issue 3, Page 632-649, March 2026.
Abstract With increased access to data and the advent of computers, the use of statistical tools and numerical simulations is becoming commonplace for ecologists. These approaches help improve our understanding of ecological phenomena and their underlying mechanisms in increasingly complex environments.
Maxime Clenet, Maxime Dion, F. Guillaume Blanchet   +2 more
wiley   +1 more source

Multistart Methods for Quantum Approximate Optimization

, 2019
Hybrid quantum-classical algorithms such as the quantum approximate optimization algorithm (QAOA) are considered one of the most promising approaches for leveraging near-term quantum computers for practical applications.
Larson, Jeffrey, Safro, Ilya, Shaydulin, Ruslan   +2 more
core   +1 more source