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 …

Quantum state tomography (QST) is a challenging task in intermediate-scale quantum
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …

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

We implement a quantum optimal control algorithm based on automatic differentiation and
harness the acceleration afforded by graphics processing units (GPUs). Automatic …

In this article we develop a continuous variable (CV) shadow tomography scheme with wide
ranging applications in quantum optics. Our work is motivated by the increasing …

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

Adaptive techniques have great potential for wide application in enhancing the precision of
quantum parameter estimation. We present an adaptive quantum state tomography protocol …

Quantum Hamiltonian identification (QHI) is important for characterizing the dynamics of
quantum systems, calibrating quantum devices, and achieving precise quantum control. In …

Quantum state tomography is a daunting challenge of experimental quantum computing,
even in moderate system size. One way to boost the efficiency of state tomography is via …