5 papers
Feedback-based quantum optimization and its classical counterpart: quantum advantage and the power of classical algorithms
Tomohiro Hattori, Takuya Hatomura
Feedback-based quantum optimization is a quantum approach to combinatorial optimization. In this paper, we introduce the classical counterpart of feedback-based quantum optimizatio…
Improving the efficiency of quantum annealing with controlled diagonal catalysts
Tomohiro Hattori, Shu Tanaka
Quantum annealing is a promising algorithm for solving combinatorial optimization problems. It searches for the ground state of the Ising model, which corresponds to the optimal so…
Frustration-Enhanced Quantum Annealing Correction Models with Additional Inter-replica Interactions
Tomohiro Hattori, Shu Tanaka
Quantum annealing correction (QAC) models provide a promising approach for mitigating errors in quantum annealers. Previous studies have established that QAC models are crucial for…
Impact of Fixing Spins in a Quantum Annealer with Energy Rescaling
Tomohiro Hattori, Hirotaka Irie, Tadashi Kadowaki +1
Quantum annealing is a promising algorithm for solving combinatorial optimization problems. However, various hardware restrictions significantly impede its efficient performance. S…
Advantages of fixing spins in quantum annealing
Tomohiro Hattori, Hirotaka Irie, Tadashi Kadowaki +1
Quantum annealing can efficiently obtain solutions to combinatorial optimization problems. Size-reduction methods are used to treat large-scale combinatorial optimization problems…