6 papers
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…
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…
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…
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…