Results 31 to 40 of about 548 (174)

Similarity-based parameter transferability in the quantum approximate optimization algorithm

Frontiers in Quantum Science and Technology, 2023
The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization.
Alexey Galda, Alexey Galda, Eesh Gupta, Eesh Gupta, Jose Falla, Jose Falla, Xiaoyuan Liu, Xiaoyuan Liu, Danylo Lykov, Yuri Alexeev, Ilya Safro   +10 more
doaj   +1 more source

Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models [PDF]

Quantum
The parity mapping provides a geometrically local encoding of the Quantum Approximate Optimization Algorithm (QAOA), at the expense of having a quadratic qubit overhead for all-to-all connected problems. In this work, we benchmark the parity-encoded QAOA
Elisabeth Wybo, Martin Leib
doaj   +1 more source

Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer

Physical Review Research, 2022
The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-classical algorithm that solves combinatorial optimization problems.
Linghua Zhu, Ho Lun Tang, George S. Barron, F. A. Calderon-Vargas, Nicholas J. Mayhall, Edwin Barnes, Sophia E. Economou   +6 more
doaj   +1 more source

Deep-Circuit QAOA

Quantum
Despite its popularity, several empirical and theoretical studies suggest that the quantum approximate optimization algorithm (QAOA) has persistent issues in providing a substantial practical advantage. Numerical results for few qubits and shallow circuits are, at best, ambiguous, and the well-studied barren plateau phenomenon draws a rather sobering ...
Gereon Koßmann, Lennart Binkowski, Lauritz van Luijk, Timo Ziegler, René Schwonnek   +4 more
openaire   +3 more sources

Parameter Setting in Quantum Approximate Optimization of Weighted Problems [PDF]

Quantum
Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization.
Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso, Shouvanik Chakrabarti, Yue Sun, Marco Pistoia   +6 more
doaj   +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

Multi-angle quantum approximate optimization algorithm

Scientific Reports, 2022
The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution.
Rebekah Herrman, Phillip C. Lotshaw, James Ostrowski, Travis S. Humble, George Siopsis   +4 more
doaj   +1 more source

Quantum approximate optimization for hard problems in linear algebra

SciPost Physics Core, 2021
The quantum approximate optimization algorithm (QAOA) by Farhi et al. is a quantum computational framework for solving quantum or classical optimization tasks.
Ajinkya Borle, Vincent E. Elfving, Samuel J. Lomonaco
doaj   +1 more source

Modularity‐Preserving Hamiltonian Compression for QAOA‐Based Community Detection

Advanced Quantum Technologies, Volume 9, Issue 6, June 2026.
This work explores several quantum methods to community detection in networks by simplifying modularity‐based Hamiltonians. By reducing circuit complexity through sparsification, spectral projection, and penalty techniques, the methods retain key structural information while improving efficiency.
Danilo Cavaliere, Giuseppe Fenza, Vincenzo Loia   +2 more
wiley   +1 more source

Simulating QAOA operation using Cirq and qsim quantum frameworks

Discrete and Continuous Models and Applied Computational Science
The problem of finding the lowest-energy state in the Ising model with a longitudinal magnetic field is studied for two- and three-dimensional lattices of various sizes using the Quantum Approximate Optimization Algorithm (QAOA).
Yuri G. Palii, Alla A. Bogolubskaya, Denis A. Yanovich   +2 more
doaj   +1 more source