Flexible learning of quantum states with generative query neural networks

Abstract

Deep neural networks are a powerful tool for the characterization of quantum states. Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized. But is it possible to train a neural network offline and to make predictions about quantum states other than the ones used for the training? Here we introduce a model of network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the states in the fiducial set. With little guidance of quantum physics, the network builds its own data-driven representation of quantum states, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representation produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter. Our network model provides a flexible approach that can be applied to online learning scenarios, where predictions must be generated as soon as experimental data become available, and to blind learning scenarios where the learner has only access to an encrypted description of the quantum hardware.

Major improvements in the presentation; new numerical experiments that illustrate the applicability of our neural network to various types of states

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• Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models
• Efficient Learning of Continuous-Variable Quantum States
• Quantum Edge Detection
• Deep Neural Network-assisted improvement of quantum compressed sensing tomography
• Machine-learning-inspired quantum control in many-body dynamics
• Artificial intelligence for representing and characterizing quantum systems