Masayuki Ohzeki

Performance Evaluation of Ising and QUBO Variable Encodings in Boltzmann Machine Learning

We compare Ising ([Formula: see text]) and QUBO ([Formula: see text]) encodings for Boltzmann machine learning under controlled protocols that fix the sampler, optimizer, and learning-rate design within each comparison. Exploiting the identity that the Fisher information matrix (...

Griffiths Inequalities and Gibbs–Bogoliubov Inequality for General Gauge Glasses with Gaussian Disorder on Nishimori Line

We consider a class of gauge glass models with Gaussian disorder on the Nishimori line, including the Ising spin glass, the XY gauge glass, the Z q gauge glass, and the gauge-invariant Potts model. We prove that the first and second Griffiths inequalities hold for these models on...

Maxwell’s Demon in Markov Chain Monte Carlo: Cooling Information Flow and Entropy Balance

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 Maxwell demon is identified with the acceptance module: it measures a proposed edge, stores the outcome...

Analysis of Critical Points in a Permutation Model on Hierarchical Lattices by Real-Space Renormalization Group

The permutation model is a classical spin system where elements of the symmetric group interact with one another. The partition function of this model is directly related to the entanglement structure of random quantum circuits and random tensor networks. In these contexts, the e...

Prediction-based sign placement using QA sampling

We present a pedestrian-flow forecasting and sign-placement workflow that leverages quantum annealing (QA) not as an optimizer, but as a sampler of individual trajectories. To reconstruct latent crowd dynamics from sparse measurements, we formulate a quadratic unconstrained binar...

Finite-Basis Duality Estimate for the Surface-Code Threshold under Correlated Bit-Flip Errors

We apply finite-basis duality to a statistical-mechanical model introduced by Chubb and Flammia for the surface code under spatially correlated bit-flip noise, for which their mapping gives a random-bond Ising model with both two-body edge interactions and four-body face interact...

Attention-Like Hebbian Learning from Quantum Probability Flow and Quantum-Annealer Tests

We propose a quantum probability-flow principle for deriving local learning rules in associative memory. A transverse field defines leakage channels from data states, and minimizing the measured survival loss gives stability-driven updates. For imaginary-time, dephased dynamics,...