The development of estimation and control theories for quantum systems is a fundamental
task for practical quantum technology. This vision article presents a brief introduction to …

The introduction of control theory in quantum mechanics has created a rich, new
interdisciplinary scientific field, which is producing novel insight into important theoretical …

We build a general quantum state tomography framework that makes use of machine
learning techniques to reconstruct quantum states from a given set of coincidence …

We revisit the application of neural networks to quantum state tomography. We confirm that
the positivity constraint can be successfully implemented with trained networks that convert …

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

Symmetric informationally complete positive operator-valued measures provide efficient
quantum state tomography in any finite dimension. In this work, we implement state …

Quantum reservoir networks combine the intelligence of neural networks with the potential of
quantum computing in a single platform. This platform operates on the architecture of …

Compensation for parameter dispersion is a significant challenge for control of
inhomogeneous quantum ensembles. In this paper, we present the systematic methodology …

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