Model-free quantum control with reinforcement learning

VV Sivak, A Eickbusch, H Liu, B Royer, I Tsioutsios… - Physical Review X, 2022 - APS
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 …

Measurement-based feedback quantum control with deep reinforcement learning for a double-well nonlinear potential

S Borah, B Sarma, M Kewming, GJ Milburn, J Twamley - Physical review letters, 2021 - APS
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 receiver enhanced by adaptive learning

C Cui, W Horrocks, S Hao, S Guha… - Light: Science & …, 2022 - nature.com
Quantum receivers aim to effectively navigate the vast quantum-state space to endow
quantum information processing capabilities unmatched by classical receivers. To date, only …

Model-free distortion canceling and control of quantum devices

AF Fouad, A Youssry, A El-Rafei… - Quantum Science and …, 2024 - iopscience.iop.org
Quantum devices need precise control to achieve their full capability. In this work, we
address the problem of controlling closed quantum systems, tackling two main issues. First …

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

MA Khan, MN Aman, B Sikdar - IEEE Access, 2024 - ieeexplore.ieee.org
The existing body of research on quantum embedding techniques is not only confined in
scope but also lacks a comprehensive understanding of the intricacies of the quantum …

Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology

F Belliardo, F Zoratti, F Marquardt, V Giovannetti - Quantum, 2024 - quantum-journal.org
Quantum sensors offer control flexibility during estimation by allowing manipulation by the
experimenter across various parameters. For each sensing platform, pinpointing the optimal …

Accelerated motional cooling with deep reinforcement learning

B Sarma, S Borah, A Kani, J Twamley - Physical Review Research, 2022 - APS
Achieving fast cooling of motional modes is a prerequisite for leveraging such bosonic
quanta for high-speed quantum information processing. In this Letter, we address the aspect …

Operating fiber networks in the quantum limit

J Nötzel, M Rosati - Journal of Lightwave Technology, 2023 - ieeexplore.ieee.org
We consider all-optical networks from a quantum perspective. We show that optimal
quantum receivers allow a decrease in energy consumption of all-optical amplifiers …

A learning theory for quantum photonic processors and beyond

M Rosati - Quantum, 2024 - quantum-journal.org
We consider the tasks of learning quantum states, measurements and channels generated
by continuous-variable (CV) quantum circuits. This family of circuits is suited to describe …

Joint-detection learning for optical communication at the quantum limit

M Rosati, A Solana - Optica Quantum, 2024 - opg.optica.org
Optical communication technology can be enhanced by using quantum signals to transfer
classical bits. This requires the message-carrying signals to interact coherently at the …