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Found 7 papers, 0 papers with code
Learning Informative Latent Representation for Quantum State Tomography
Our method leverages a transformer-based encoder to extract an informative latent representation (ILR) from imperfect measurement data and employs a decoder to predict the quantum states based on the ILR.
Tomography of Quantum States from Structured Measurements via quantum-aware transformer
Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices.
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control
Due to its property of not requiring prior knowledge of the environment, reinforcement learning has significant potential for quantum control problems.
Deep Reinforcement Learning with Quantum-inspired Experience Replay
In this paper, a novel training paradigm inspired by quantum computation is proposed for deep reinforcement learning (DRL) with experience replay.
Curriculum-based Deep Reinforcement Learning for Quantum Control
Deep reinforcement learning has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape.
On compression rate of quantum autoencoders: Control design, numerical and experimental realization
Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information.
Learning-based Quantum Robust Control: Algorithm, Applications and Experiments
Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems.
