Embedding Overhead Scaling of Optimization Problems in Quantum Annealing PRX Quantum 2, 040322 (2021) https://doi.org/10.1103/PRXQuantum.2.04032
We study large-scale applications using a GPU-accelerated version of the massively parallel Jülich u...
Quantum annealers aim at solving nonconvex optimization problems by exploiting cooperative tunneling...
To date, conventional computers have never been able to efficiently handle certain tasks, where the ...
In order to treat all-to-all-connected quadratic binary optimization problems (QUBOs) with hard- war...
Quantum computing aims to harness the properties of quantum systems to more effectively solve certai...
Quantum annealing belongs to a family of quantum optimization algorithms designed to solve combinato...
There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum ...
Quantum computing has the potential to revolutionize the way hard computational problems are solved ...
International audienceWe are interested in Quantum Annealing (QA), an algorithm inspired by quantum ...
In recent years, quantum annealing has gained the status of being a promising candidate for solving ...
Quantum annealing has the potential to find low energy solutions of NP-hard problems that can be exp...
The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers...
In recent years, quantum annealing has gained the status of being a promising candidate for solving ...
The observation of an unequivocal quantum speedup remains an elusive objective for quantum computing...
In many real-life situations in engineering (and in other disciplines), we need to solve an optimiza...
We study large-scale applications using a GPU-accelerated version of the massively parallel Jülich u...
Quantum annealers aim at solving nonconvex optimization problems by exploiting cooperative tunneling...
To date, conventional computers have never been able to efficiently handle certain tasks, where the ...
In order to treat all-to-all-connected quadratic binary optimization problems (QUBOs) with hard- war...
Quantum computing aims to harness the properties of quantum systems to more effectively solve certai...
Quantum annealing belongs to a family of quantum optimization algorithms designed to solve combinato...
There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum ...
Quantum computing has the potential to revolutionize the way hard computational problems are solved ...
International audienceWe are interested in Quantum Annealing (QA), an algorithm inspired by quantum ...
In recent years, quantum annealing has gained the status of being a promising candidate for solving ...
Quantum annealing has the potential to find low energy solutions of NP-hard problems that can be exp...
The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers...
In recent years, quantum annealing has gained the status of being a promising candidate for solving ...
The observation of an unequivocal quantum speedup remains an elusive objective for quantum computing...
In many real-life situations in engineering (and in other disciplines), we need to solve an optimiza...
We study large-scale applications using a GPU-accelerated version of the massively parallel Jülich u...
Quantum annealers aim at solving nonconvex optimization problems by exploiting cooperative tunneling...
To date, conventional computers have never been able to efficiently handle certain tasks, where the ...
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