A review on quantum approximate optimization algorithm and its variants
Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …
variational quantum algorithm that aims to solve combinatorial optimization problems that …
Challenges and opportunities in quantum optimization
Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …
force classical simulation. Interest in quantum algorithms has developed in many areas …
Quantum optimization of maximum independent set using Rydberg atom arrays
Realizing quantum speedup for practically relevant, computationally hard problems is a
central challenge in quantum information science. Using Rydberg atom arrays with up to …
central challenge in quantum information science. Using Rydberg atom arrays with up to …
Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm
for solving optimization problems on quantum computers. However, the potential of QAOA to …
for solving optimization problems on quantum computers. However, the potential of QAOA to …
Limitations of variational quantum algorithms: a quantum optimal transport approach
The impressive progress in quantum hardware of the last years has raised the interest of the
quantum computing community in harvesting the computational power of such devices …
quantum computing community in harvesting the computational power of such devices …
The quantum approximate optimization algorithm at high depth for maxcut on large-girth regular graphs and the sherrington-kirkpatrick model
The Quantum Approximate Optimization Algorithm (QAOA) finds approximate solutions to
combinatorial optimization problems. Its performance monotonically improves with its depth …
combinatorial optimization problems. Its performance monotonically improves with its depth …
Trainability enhancement of parameterized quantum circuits via reduced-domain parameter initialization
Parameterized quantum circuits (PQCs) have been widely used as a machine learning
model to explore the potential of achieving quantum advantages for various tasks. However …
model to explore the potential of achieving quantum advantages for various tasks. However …
NISQ computers: a path to quantum supremacy
M AbuGhanem, H Eleuch - IEEE Access, 2024 - ieeexplore.ieee.org
The quest for quantum advantage, wherein quantum computers surpass the computational
capabilities of classical computers executing state-of-the-art algorithms on well-defined …
capabilities of classical computers executing state-of-the-art algorithms on well-defined …
Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models
The Quantum Approximate Optimization Algorithm (QAOA) is a general purpose quantum
algorithm designed for combinatorial optimization. We analyze its expected performance …
algorithm designed for combinatorial optimization. We analyze its expected performance …
Recursive greedy initialization of the quantum approximate optimization algorithm with guaranteed improvement
The quantum approximate optimization algorithm (QAOA) is a variational quantum
algorithm, where a quantum computer implements a variational ansatz consisting of p layers …
algorithm, where a quantum computer implements a variational ansatz consisting of p layers …