Sequence-Model-Guided Measurement Selection for Quantum State Learning
This work addresses the challenge of scalable quantum state characterization for researchers in quantum science and technology, representing an incremental improvement through a novel method for a known bottleneck.
The paper tackles the problem of selecting efficient measurements for learning quantum states, which becomes intractable for large systems, by introducing a deep neural network with a sequence model architecture that adaptively searches for measurement choices. The result shows that this model consistently outperforms uniformly random choices across tasks like predicting linear/nonlinear properties, state clustering, and tomography, and it tends to recommend boundary measurements for topological systems, suggesting an independent discovery of boundary-bulk connections.
Characterization of quantum systems from experimental data is a central problem in quantum science and technology. But which measurements should be used to gather data in the first place? While optimal measurement choices can be worked out for small quantum systems, the optimization becomes intractable as the system size grows large. To address this problem, we introduce a deep neural network with a sequence model architecture that searches for efficient measurement choices in a data-driven, adaptive manner. The model can be applied to a variety of tasks, including the prediction of linear and nonlinear properties of quantum states, as well as state clustering and state tomography tasks. In all these tasks, we find that the measurement choices identified by our neural network consistently outperform the uniformly random choice. Intriguingly, for topological quantum systems, our model tends to recommend measurements at the system's boundaries, even when the task is to predict bulk properties. This behavior suggests that the neural network may have independently discovered a connection between boundaries and bulk, without having been provided any built-in knowledge of quantum physics.