activity
20202025
most citedBoltzmann machine learning with a variational quantum algorithm

24 citations · 40 across the 10 of their papers we have counts for

collaborators

11 papers

quant-ph2025

Theoretical Analysis of Photonic Resonances in Spectroscopic Measurements of a Kerr Nonlinear Resonator

Yuki Tanaka, Aiko Yamaguchi, Tomohiro Yamaji +4

The Kerr parametric oscillator (KPO) has recently attracted considerable attention from the perspective of its applications to quantum information processing, and understanding its…

quant-ph2025★ 1 cited

Hardware-efficient quantum annealing with error mitigation via classical shadow

Takaharu Yoshida, Yuta Shingu, Chihaya Shimada +3

Quantum annealing (QA) is an efficient method for finding the ground-state energy of the problem Hamiltonian. However, in practical implementation, the system suffers from decohere…

quant-ph2025

Variational quantum-neural hybrid imaginary time evolution

Hiroki Kuji, Tetsuro Nikuni, Yuta Shingu

Numerous methodologies have been proposed to implement imaginary-time evolution (ITE) on quantum computers. Among these, variational ITE (VITE) methods for noisy intermediate-scale…

quant-ph2025

Geometrical scheduling of adiabatic control without information of energy spectra

Yuta Shingu, Takuya Hatomura

Adiabatic control is a fundamental technique for manipulating quantum systems, guided by the quantum adiabatic theorem, which ensures suppressed nonadiabatic transitions under slow…

quant-ph2024

Robust phase estimation of the ground-state energy without controlled time evolution on a quantum device

Hiroki Kuji, Yuta Shingu, Tetsuro Nikuni +3

Estimating the ground-state energy of Hamiltonians in quantum systems is an important task. In this work, we demonstrate that the ground-state energy can be accurately estimated wi…

quant-ph2024

Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches

Santaro Kikuura, Ryoya Igata, Yuta Shingu +1

This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems: max-cut, number partitioning, knapsack, and…

quant-ph2024

Single-qubit rotations on a binomial code without ancillary qubits

Yuki Tanaka, Yuichiro Mori, Yuta Shingu +3

Great attention has been paid to binomial codes utilizing bosonic systems as logical qubits with error correction capabilities. However, implementing single-qubit rotation operatio…

quant-ph2022

Quantum annealing with error mitigation

Yuta Shingu, Tetsuro Nikuni, Shiro Kawabata +1

Quantum annealing (QA) is one of the efficient methods to calculate the ground-state energy of a problem Hamiltonian. In the absence of noise, QA can accurately estimate the ground…

quant-ph2021★ 5 cited

Calculation of Gibbs partition function with imaginary time evolution on near-term quantum computers

Keisuke Matsumoto, Yuta Shingu, Suguru Endo +5

The Gibbs partition function is an important quantity in describing statistical properties of a system in thermodynamic equilibrium. There are several proposals to calculate the pa…

quant-ph2021★ 10 cited

Variational secure cloud quantum computing

Yuta Shingu, Yuki Takeuchi, Suguru Endo +5

Variational quantum algorithms (VQAs) have been considered to be useful applications of noisy intermediate-scale quantum (NISQ) devices. Typically, in the VQAs, a parametrized ansa…

quant-ph2020★ 24 cited

Boltzmann machine learning with a variational quantum algorithm

Yuta Shingu, Yuya Seki, Shohei Watabe +5

Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data. A thermal equilibrium state is typically used for Boltzmann machine learn…