Artificial intelligence and machine learning for quantum technologies
M Krenn, J Landgraf, T Foesel, F Marquardt - Physical Review A, 2023 - APS
In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …
of science and technology significantly. In the present perspective article, we explore how …
[HTML][HTML] A tutorial on optimal control and reinforcement learning methods for quantum technologies
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 …
the development of Quantum Technologies. In recent years, Machine Learning techniques …
Experimental deep reinforcement learning for error-robust gate-set design on a superconducting quantum computer
Quantum computers promise tremendous impact across applications—and have shown
great strides in hardware engineering—but remain notoriously error prone. Careful design of …
great strides in hardware engineering—but remain notoriously error prone. Careful design of …
Model-free quantum control with reinforcement learning
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 …
control, which relies on a system simulation for optimization of control policies. To overcome …
Modern applications of machine learning in quantum sciences
In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …
advances in the application of machine learning methods in quantum sciences. We cover …
[HTML][HTML] Deep reinforcement learning for quantum multiparameter estimation
Estimation of physical quantities is at the core of most scientific research, and the use of
quantum devices promises to enhance its performances. In real scenarios, it is fundamental …
quantum devices promises to enhance its performances. In real scenarios, it is fundamental …
Realizing a deep reinforcement learning agent for real-time quantum feedback
K Reuer, J Landgraf, T Fösel, J O'Sullivan… - Nature …, 2023 - nature.com
Realizing the full potential of quantum technologies requires precise real-time control on
time scales much shorter than the coherence time. Model-free reinforcement learning …
time scales much shorter than the coherence time. Model-free reinforcement learning …
Deep reinforcement learning for quantum state preparation with weak nonlinear measurements
Quantum control has been of increasing interest in recent years, eg for tasks like state
initialization and stabilization. Feedback-based strategies are particularly powerful, but also …
initialization and stabilization. Feedback-based strategies are particularly powerful, but also …
Approximate autonomous quantum error correction with reinforcement learning
Autonomous quantum error correction (AQEC) protects logical qubits by engineered
dissipation and thus circumvents the necessity of frequent, error-prone measurement …
dissipation and thus circumvents the necessity of frequent, error-prone measurement …
Gradient-ascent pulse engineering with feedback
Efficient approaches to quantum control and feedback are essential for quantum
technologies, from sensing to quantum computation. Open-loop control tasks have been …
technologies, from sensing to quantum computation. Open-loop control tasks have been …