Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Abstract Quantum Optimal Control is an established field of research which is necessary for
the development of Quantum Technologies. In recent years, Machine Learning techniques …

Model bias is an inherent limitation of the current dominant approach to optimal quantum
control, which relies on a system simulation for optimization of control policies. To overcome …

Closed loop quantum control uses measurement to control the dynamics of a quantum
system to achieve either a desired target state or target dynamics. In the case when the …

Quantum many-body control is a central milestone en route to harnessing quantum
technologies. However, the exponential growth of the Hilbert space dimension with the …

In this work, we put forward an approach of constructing the optimal composite pulse
sequence for robust population transfer in two-level systems. This approach is quite …

We deploy a combination of reinforcement learning-based approaches and more traditional
optimization techniques to identify optimal protocols for population transfer in a multi-level …

In the noisy intermediate-scale quantum era, optimal digitized pulses are requisite for
efficient quantum control. This goal is translated into dynamic programming, in which a deep …

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 …

Deep reinforcement learning (DRL) has been recognized as an efficient technique to design
optimal strategies for different complex systems without prior knowledge of the control …