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Learning-Driven Annealing with Adaptive Hamiltonian Modification for Solving Large-Scale Problems on Quantum Devices [PDF]
QuantumWe present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as short annealing times and integrated control errors.
Sebastian Schulz +2 more
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Advanced anneal paths for improved quantum annealing [PDF]
2020 IEEE International Conference on Quantum Computing and Engineering (QCE), 2020 Advances in quantum annealing technology make it possible to obtain high quality approximate solutions of important NP-hard problems. With the newer generations of the D-Wave annealer, more advanced features are available which allow the user to have greater control of the anneal process.
Elijah Pelofske +2 more
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How to Solve Combinatorial Optimization Problems Using Real Quantum Machines: A Recent Survey
IEEE Access, 2022 A combinatorial optimization problem (COP) is the problem of finding the optimal solution in a finite set. When the size of the feasible solution set is large, the complexity of the problem increases, and it is not easy to solve in a reasonable time with
Sovanmonynuth Heng +3 more
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Travel time optimization on multi-AGV routing by reverse annealing
Scientific Reports, 2022 Quantum annealing has been actively researched since D-Wave Systems produced the first commercial machine in 2011. Controlling a large fleet of automated guided vehicles is one of the real-world applications utilizing quantum annealing. In this study, we
Renichiro Haba +2 more
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CoRR, 2014 Brief description on the state of the art of some local optimization methods: Quantum annealing Quantum annealing (also known as alloy, crystallization or tempering) is analogous to simulated annealing but in substitution of thermal activation by quantum tunneling.
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Deep learning optimal quantum annealing schedules for random Ising models
New Journal of Physics, 2023 A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory neural networks to automate the search for ...
Pratibha Raghupati Hegde +3 more
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Expanding the scope of quantum annealing applicability
AIP Advances, 2023 With the emergence of D-wave’s quantum annealing machine, the power of quantum computing has become tangible, and the development of quantum computers is accelerating rapidly.
Hiroshi Isshiki, Koki Asari
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Convergence of Quantum Annealing with Real-Time Schrodinger Dynamics [PDF]
, 2007 Convergence conditions for quantum annealing are derived for optimization problems represented by the Ising model of a general form. Quantum fluctuations are introduced as a transverse field and/or transverse ferromagnetic interactions, and the time ...
Das A. +17 more
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Quantum Annealing and Analog Quantum Computation [PDF]
, 2008 We review here the recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations.
Aeppli, G. +26 more
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Quantum optimization of complex systems with a quantum annealer
Physical Review A, 2022 We perform an in-depth comparison of quantum annealing with several classical optimisation techniques, namely thermal annealing, Nelder-Mead, and gradient descent. We begin with a direct study of the 2D Ising model on a quantum annealer, and compare its properties directly with those of the thermal 2D Ising model. These properties include an Ising-like
Steve Abel +2 more
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