3 citations · 7 across the 6 of their papers we have counts for
6 papers
Factorization Machine with Quadratic-Optimization Annealing for RNA Inverse Folding and Evaluation of Binary-Integer Encoding and Nucleotide Assignment
Shuta Kikuchi, Shu Tanaka
The RNA inverse folding problem aims to identify nucleotide sequences that preferentially adopt a given target secondary structure. While various heuristic and machine learning-bas…
Effectiveness of Binary Autoencoders for QUBO-Based Optimization Problems
Tetsuro Abe, Masashi Yamashita, Shu Tanaka
In black-box combinatorial optimization, objective evaluations are often expensive, so high quality solutions must be found under a limited budget. Factorization machine with quant…
Utilizing intermediate states in quantum annealing for multi-objective optimization
Keita Takahashi, Shu Tanaka
We investigate obtaining intermediate quantum states during the quantum annealing process to address the limitation of the linear weighted sum method in multi-objective optimizatio…
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…
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…
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…