The goal of machine learning is to facilitate a computer to execute a specific task without
explicit instruction by an external party. Quantum foundations seek to explain the conceptual …

Machine learning is a field of computer science that seeks to build computers capable of
discovering meaningful information and making predictions about data. It is the core of …

Generative adversarial learning is one of the most exciting recent breakthroughs in machine
learning. It has shown splendid performance in a variety of challenging tasks such as image …

Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of
machine learning approaches in adversarial settings and develo** techniques …

Quantum compiling, a process that decomposes the quantum algorithm into a series of
hardware-compatible commands or elementary gates, is of fundamental importance for …

Tensor networks are efficient representations of high-dimensional tensors with widespread
applications in quantum many-body physics. Recently, they have been adapted to the field …

The main power of quantum sensors is achieved when the probe is composed of several
particles. In this situation, quantum features such as entanglement contribute in enhancing …

Cross-platform verification, a critical undertaking in the realm of early-stage quantum
computing, endeavors to characterize the similarity of two imperfect quantum devices …

Representation by neural networks, in particular by restricted Boltzmann machines (RBMs),
has provided a powerful computational tool to solve quantum many-body problems. An …

Cellular mechanical metamaterials are a special class of materials whose mechanical
properties are primarily determined by their geometry. However, capturing the nonlinear …