Neural network state estimation for full quantum state tomography

Abstract

An efficient state estimation model, neural network estimation (NNE), empowered by machine learning techniques, is presented for full quantum state tomography (FQST). A parameterized function based on neural network is applied to map the measurement outcomes to the estimated quantum states. Parameters are updated with supervised learning procedures. From the computational complexity perspective our algorithm is the most efficient one among existing state estimation algorithms for full quantum state tomography. We perform numerical tests to prove both the accuracy and scalability of our model.

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• Machine learning and the physical sciences
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• Flexible learning of quantum states with generative query neural networks
• On the experimental feasibility of quantum state reconstruction via machine learning
• Quantum-tailored machine-learning characterization of a superconducting qubit
• Quantum State Tomography with Conditional Generative Adversarial Networks
• Variational Approach to Quantum State Tomography based on Maximal Entropy Formalism
• Demonstration of machine-learning-enhanced Bayesian quantum state estimation
• tqix: A toolbox for Quantum in X: Quantum measurement, quantum tomography, quantum metrology, and others
• Observing Schrödinger's Cat with Artificial Intelligence: Emergent Classicality from Information Bottleneck
• Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model