The main goal of machine learning is the creation of self-learning algorithms in many areas
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …

How well can we approximate a quantum channel output state using a random codebook
with a certain size? In this work, we study the quantum soft covering problem, which uses a …

In this paper, we establish an interesting duality between two different quantum information-
processing tasks, namely, classical source coding with quantum side information, and …

Quantum relative entropy programs are convex optimization problems which minimize a
linear functional over an affine section of the epigraph of the quantum relative entropy …

We show that the communication cost of quantum broadcast channel simulation under free
entanglement assistance between the sender and the receivers is asymptotically …

Quantum relative entropies are jointly convex functions of two positive definite matrices that
generalize the Kullback–Leibler divergence and arise naturally in quantum information …

We propose a new randomized method for solving systems of nonlinear equations, which
can find sparse solutions or solutions under certain simple constraints. The scheme only …

The Blahut-Arimoto algorithm is a well-known method to compute classical channel
capacities and rate-distortion functions. Recent works have extended this algorithm to …

We establish a one-shot strong converse bound for privacy amplification against quantum
side information with trace distance as a security criterion. Firstly, our result shows that the …

Rényi and Augustin information are generalizations of mutual information defined via the
Rényi divergence, playing a significant role in evaluating the performance of information …