5 citations · 5 across the 2 of their papers we have counts for
8 papers
Maxwell's Demon in Markov Chain Monte Carlo: Cooling Information Flow and Entropy Balance
Masayuki Ohzeki
Markov chain Monte Carlo algorithms can be viewed as feedback devices that compare a proposed move with the target distribution and then accept or reject it. In this paper the Maxw…
Dynamical Criticality of a Machine-learning-assisted Monte Carlo algorithm for a Mean-Field Spin Glass model
Seiya Miyamoto, Masayuki Ohzeki, Yoshihiko Nishikawa
We critically assess the performance of an autoregressive generative neural network model by applying it to an antiferromagnetic Ising model on a random regular graph. We train the…
Kernel Learning by quantum annealer
Yasushi Hasegawa, Hiroki Oshiyama, Masayuki Ohzeki
The Boltzmann machine is one of the various applications using quantum annealer. We propose an application of the Boltzmann machine to the kernel matrix used in various machine-lea…
Graph minor embedding can affect sampling degenerate ground states using quantum annealing
Naoki Maruyama, Masayuki Ohzeki, Kazuyuki Tanaka
Quantum annealing, as currently implemented in hardware, cannot fairly sample all ground states. Graph minor embedding, which maps a problem to the hardware graph of quantum anneal…
Online calibration scheme for training restricted Boltzmann machines with quantum annealing
Takeru Goto, Masayuki Ohzeki
We propose a scheme to calibrate the internal parameters of a quantum annealer to obtain well-approximated samples for training a restricted Boltzmann machine (RBM). Empirically, s…
Quadratic Unconstrained Binary Formulation for Traffic Signal Optimization on Real-World Maps
Reo Shikanai, Masayuki Ohzeki, Kazuyuki Tanaka
The D-Wave quantum annealing machine can quickly find the optimal solution for quadratic unconstrained binary optimization (QUBO). One of the applications where the use of quantum…