With quantum computing technologies nearing the era of commercialization and quantum
supremacy, machine learning (ML) appears as one of the promising'killer'applications …

Inspired by the success of Boltzmann machines based on classical Boltzmann distribution,
we propose a new machine-learning approach based on quantum Boltzmann distribution of …

The new era of artificial intelligence (AI) aims to entangle the relationships among models
(characterizations), algorithms, and implementations toward the high‐level intelligence with …

This monograph aims at providing an introduction to key concepts, algorithms, and
theoretical results in machine learning. The treatment concentrates on probabilistic models …

Mainstream machine-learning techniques such as deep learning and probabilistic
programming rely heavily on sampling from generally intractable probability distributions …

Inference is the task of drawing conclusions about unobserved variables given observations
of related variables. Applications range from identifying diseases from symptoms to …

Quantum compiling fills the gap between the computing layer of high-level quantum
algorithms and the layer of physical qubits with their specific properties and constraints …

A network of Kerr-nonlinear parametric oscillators without dissipation has recently been
proposed for solving combinatorial optimization problems via quantum adiabatic evolution …

Abstract Restricted Boltzmann machines (RBMs) constitute a class of neural networks for
unsupervised learning with applications ranging from pattern classification to quantum state …

In-vehicle controller area network (CAN) is susceptible to various cyberattacks due to its
broadcast-based communication nature. An attacker can inject false messages to a vehicle's …