Advantages of fixing spins in quantum annealing
arXiv:2410.21924 · doi:10.7566/JPSJ.94.013001
Abstract
Quantum annealing can efficiently obtain solutions to combinatorial optimization problems. Size-reduction methods are used to treat large-scale combinatorial optimization problems that cannot be input directly into a quantum annealer because of its size limitation. Various size-reduction methods using fixing spins have been proposed as quantum-classical hybrid methods to obtain solutions. However, the high performance of these hybrid methods is yet to be clearly elucidated. In this study, we adopted a parameterized fixing spins method to verify the effects of fixing spins. The results revealed that setting the appropriate number of spins of the subproblem is crucial for obtaining a satisfactory solution, and the energy gap expansion is confirmed after fixing spins.
References in corpus (17)
- Quantum Annealing in the Transverse Ising Model
- Quantum Annealing and Analog Quantum Computation
- Quantum Annealing: A New Method for Minimizing Multidimensional Functions
- Reverse Quantum Annealing Approach to Portfolio Optimization Problems
- A Hybrid Solution Method for the Capacitated Vehicle Routing Problem Using a Quantum Annealer
- Expanding the horizon of automated metamaterials discovery via quantum annealing
- Scaling and diabatic effects in quantum annealing with a D-Wave device
- Benchmarking Quantum Annealing Controls with Portfolio Optimization
- Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods
- Application of QUBO solver using black-box optimization to structural design for resonance avoidance
- Towards optimization of photonic-crystal surface-emitting lasers via quantum annealing
- Hybrid Quantum Annealing via Molecular Dynamics
- Solving Larger Maximum Clique Problems Using Parallel Quantum Annealing
- Boosting quantum annealer performance via sample persistence
- Annealing-Assisted Column Generation for Inequality-Constrained Combinatorial Optimization Problems
- Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programming
- Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer