We apply deep-neural-network-based techniques to quantum state classification and
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …

Conventional methods for computing maximum-likelihood estimators (MLE) often converge
slowly in practical situations, leading to a search for simplifying methods that rely on …

We present an algorithm for projecting superoperators onto the set of completely positive,
trace-preserving maps. When combined with gradient descent of a cost function, the …

Reconstructing the state of quantum many-body systems is of fundamental importance in
quantum information tasks, but extremely challenging due to the curse of dimensionality. In …

Accurate quantum tomography is a vital tool in both fundamental and applied quantum
science. It is a task that involves processing a noisy measurement record in order to …

With nowadays steadily growing quantum processors, it is required to develop new quantum
tomography tools that are tailored for high-dimensional systems. In this work, we describe …

We want to start with few remarks predating considerably the emerging of the idea for writing
this book and our collaboration in general. They are related to the first author's own …

We investigate and ascertain the ideal inputs to any finite-time physical process. We
demonstrate that the expectation values of entropy flow, heat, and work can all be …

We propose a new quantum state reconstruction method that combines ideas from
compressed sensing, non-convex optimization, and acceleration methods. The algorithm …

In this paper, we study the online quantum state learning problem which is recently
proposed by Aaronson et al.(2018). In this problem, the learning algorithm sequentially …