output
20042026
most citedReduced carbon solubility in Fe nano-clusters and implications for the growth of single-walled carbon nanotubes

90 citations

Showing quant-phShow all

7 papers · 1 filter

quant-ph2025

Demonstration of logical quantum phase estimation for X-ray absorption spectra

Hirofumi Nishi, Taichi Kosugi, Satoshi Hirose +2

In this study, we employed Fourier-based quantum phase estimation (QPE) to calculate X-ray absorption spectroscopy (XAS) spectra. The primary focus of this study is the calculation…

quant-ph2024★ 3 cited

Subgradient Method using Quantum Annealing for Inequality-Constrained Binary Optimization Problems

Taisei Takabayashi, Takeru Goto, Masayuki Ohzeki

Quantum annealing is a generic solver for combinatorial optimization problems that utilizes quantum fluctuations. Recently, there has been extensive research applying quantum annea…

quant-ph2024★ 3 cited

Inequality constraints in variational quantum circuits with qudits

Alberto Bottarelli, Sebastian Schmitt, Philipp Hauke

Quantum optimization is emerging as a prominent candidate for exploiting the capabilities of near-term quantum devices. Many application-relevant optimization tasks require the inc…

quant-ph2023★ 11 cited

Variational Quantum Multi-Objective Optimization

Linus Ekstrom, Hao Wang, Sebastian Schmitt

Solving combinatorial optimization problems on near-term quantum devices has gained a lot of attraction in recent years. Currently, most works have focused on single-objective prob…

quant-ph2023★ 8 cited

A Hybrid Quantum-Classical Approach to the Electric Mobility Problem

Margarita Veshchezerova, Mikhail Somov, David Bertsche +4

We suggest a hybrid quantum-classical routine for the NP-hard Electric Vehicle Fleet Charging and Allocation Problem. The original formulation is a Mixed Integer Linear Program wit…

quant-ph2023★ 6 cited

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…