This Perspective article aims to emphasize the crucial role of uncertainty quantification (UQ)
in understanding magnetic phase transitions, which are pivotal in various applications …

Boltzmann machine is a powerful machine learning model with many real-world
applications, for example by constructing deep belief networks. Statistical inference on a …

We propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful
energy-based neural network. It can be realized experimentally on a continuous-variable …

This paper describes the components and configurations available in a new quantum-
assisted machine learning (QAML) framework. QAML is an open source package that …

We study the design and optimization of the Boltzmann machine hardware using
superconducting circuits as a new stochastic information processing method. The Boltzmann …

Quantum-assisted training of machine learning models makes use of the sampling
capabilities of quantum computing devices to approximate the probability distribution of …

Motivated by the potential performance gains offered by quantum computing (QC), a recent
research focus—known as quantum-assisted machine learning (QAML)—aims to develop …

A mixed-integer linear programming (MILP) formulation is presented for parameter
estimation of the Potts model. Two algorithms are developed; the first method estimates the …

Abstract Variational Quantum Eigensolver (VQE) addresses challenges that conventional
computation struggles to overcome. However, VQE lacks the ability to autonomously trend …

Abstract A Quantum Annealing Processor (QAP) is a specialized quantum computing device
that can solve optimization problems that are hard to solve using classical systems. To use a …