The study, led by Prof. Daoyi Dong of the Australian Artificial Intelligence Institute at the University of Technology Sydney, Australia, and Dr. Bo Qi of the State Key Laboratory of Mathematical Sciences, Chinese Academy of Sciences, China, sheds new light on how these data-driven methods can help address some of the key challenges in developing practical and scalable quantum technologies.
Accurate characterization and precise control of quantum systems are becoming increasingly crucial as quantum computing, simulation, and sensing continue to advance. Due to system complexity, noise, and restricted access to system models, traditional approaches frequently have drawbacks. The authors emphasize how adaptive, data-driven solutions provided by machine learning might improve the efficiency and resilience of quantum processes.
The study discusses a wide range of machine learning algorithms used in quantum estimation problems, including ways to reconstruct quantum systems' states or dynamics from measurement data. Neural networks, generative models, and attention-based architectures like Transformers have all shown potential in quantum tomography applications.
One especially intriguing theory is the comparison between language modeling and quantum estimation, which proposes that reconstructing a quantum state from structured observations is similar to assembling a sentence from characters and words.
In terms of quantum control, the study describes how learning-based algorithms can optimize control strategies given actual limitations. Gradient-based strategies have been demonstrated to increase control fidelity and resilience when combined with other data-driven approaches.
Evolutionary algorithms are recognized for optimizing quantum systems without requiring explicit physical models. The authors presented experimental examples employing femtosecond laser pulses, in which the algorithms maximize selective control of molecular fragmentation while increasing resilience to parameter fluctuations.
The review delves deeper into reinforcement learning methodologies that enable autonomous control via trial-and-error interactions with quantum systems. These methodologies' model-free and adaptable nature makes them ideal for dealing with complicated scenarios involving uncertain system dynamics or partial observability.
A significant focus is on quantum error correction, a prerequisite for fault-tolerant quantum computing. The authors discuss recent advances in using reinforcement learning for adaptive quantum error correction, in which agents learn to choose quantum gates or measurements based on real-time feedback.
This study combines AI with quantum engineering to present a possible path for intelligent quantum systems that are both scalable and robust. It is a timely resource for researchers working to incorporate machine learning into the design, estimation, and control of next-generation quantum devices.
Journal Reference:
Ma, H., et al. (2025) Machine Learning for Estimation and Control of Quantum Systems. National Science Review. doi.org/10.1093/nsr/nwaf269.