1 citations · 1 across the 10 of their papers we have counts for
13 papers
Residual Fisher Information in Measurement-Error Uncertainty
Masayuki Ohzeki, Taiga Suzuki
Measurement error in the Watanabe-Sagawa-Ueda formulation is the excess estimation error of a classical record over the quantum statistical limit. We show that, at the quantum-inst…
Simultaneous Perturbation as a Spectral Filter
Masayuki Ohzeki
Parameterized quantum circuits require repeated measurements to estimate objective gradients, making the number of circuit evaluations a central resource. Simultaneous perturbation…
Information Erasure and Quantum Imprint in Quantum Measurement and First-Order SPAM Error Separation
Taiga Suzuki, Yuki Ito, Masayuki Ohzeki
We introduce information erasure and quantum imprint as two properties that classify quantum instruments. Information erasure is the property that an appropriate postselection can…
Boundary-Canceled Score-Hamiltonian Sampling
Masayuki Ohzeki
Diffusion sampling is related to imaginary-time quantum annealing through a forward/backward Schrödinger equation pair and its one-sided counterdiabatic connection. For a smooth, g…
Simulation-free and finite-time diffusion model
Kentaro Kaba, Masayuki Ohzeki, Yuki Sughiyama
The performance of generative diffusion models is determined by the choice of the reference diffusion process connecting the empirical and prior distributions. Conventional approac…
Population Annealing as a Discrete-Time Schrödinger Bridge
Masayuki Ohzeki
We present a theoretical framework that reinterprets Population Annealing (PA) through the lens of the discrete-time Schrödinger Bridge (SB) problem. We demonstrate that the heuris…
Kernel Learning for Regression via Quantum Annealing Based Spectral Sampling
Yasushi Hasegawa, Masayuki Ohzeki
While quantum annealing (QA) has been developed for combinatorial optimization, practical QA devices operate at finite temperature and under noise, and their outputs can be regarde…
Storage capacity of perceptron with variable selection
Yingying Xu, Masayuki Ohzeki, Yoshiyuki Kabashima
A central challenge in machine learning is to distinguish genuine structure from chance correlations in high-dimensional data. In this work, we address this issue for the perceptro…
Performance Evaluation of Ising and QUBO Variable Encodings in Boltzmann Machine Learning
Yasushi Hasegawa, Masayuki Ohzeki
We compare Ising ({-1, +1}) and QUBO ({0, 1}) encodings for Boltzmann machine learning under controlled protocols that fix the sampler, optimizer, and learning-rate design within e…
Quantum State Recovery via Direct Sum Formalism Without Measurement Outcomes
Taiga Suzuki, Masayuki Ohzeki
This study proposes a new approach to quantum state recovery following measurement. Specifically, we introduce a special operation that transfers the probability amplitude of the q…
Quantum generative model on bicycle-sharing system and an application
Fumio Nemoto, Nobuyuki Koike, Daichi Sato +2
Recently, bicycle-sharing systems have been implemented in numerous cities, becoming integral to daily life. However, a prevalent issue arises when intensive commuting demand leads…
Schödinger Bridge Type Diffusion Models as an Extension of Variational Autoencoders
Kentaro Kaba, Reo Shimizu, Masayuki Ohzeki +1
Generative diffusion models use time-forward and backward stochastic differential equations to connect the data and prior distributions. While conventional diffusion models (e.g.,…
Statistical Mechanics Calculations Using Variational Autoregressive Networks and Quantum Annealing
Yuta Tamura, Masayuki Ohzeki
In statistical mechanics, computing the partition function is generally difficult. An approximation method using a variational autoregressive network (VAN) has been proposed recent…