1 citations · 1 across the 2 of their papers we have counts for
6 papers
Subsampling Factorization Machine Annealing
Yusuke Hama, Tadashi Kadowaki
Quantum computing and machine learning are state-of-the-art technologies that have been investigated intensively in both academia and industry. The hybrid technology of these two i…
Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning
Akitada Sakurai, Aoi Hayashi, Tadayoshi Matsumori +3
Quantum annealing is typically regarded as a tool for combinatorial optimization, but its coherent dynamics also offer potential for machine learning. We present a model that encod…
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…
Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering
Tadashi Kadowaki
Recent advances in artificial intelligence (AI) and quantum computing are accelerating automation in scientific and engineering processes, fundamentally reshaping research methodol…
Quantum Annealing Enhanced Markov-Chain Monte Carlo
Shunta Arai, Tadashi Kadowaki
In this study, we propose quantum annealing-enhanced Markov Chain Monte Carlo (QAEMCMC), where QA is integrated into the MCMC subroutine. QA efficiently explores low-energy configu…
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…