Quantum-tailored machine-learning characterization of a superconducting qubit

Abstract

Machine learning (ML) is a promising approach for performing challenging quantum-information tasks such as device characterization, calibration and control. ML models can train directly on the data produced by a quantum device while remaining agnostic to the quantum nature of the learning task. However, these generic models lack physical interpretability and usually require large datasets in order to learn accurately. Here we incorporate features of quantum mechanics in the design of our ML approach to characterize the dynamics of a quantum device and learn device parameters. This physics-inspired approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data obtained from continuous weak measurement of a driven superconducting transmon qubit. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task, thus laying the groundwork for more scalable characterization techniques.

15 pages, 9 figures

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• Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning
• Noise Classification in Three-Level Quantum Networks by Machine Learning
• Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements
• Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics
• Differentiable master equation solver for quantum device characterisation
• Quantum feedback control with a transformer neural network architecture
• Learning interactions between Rydberg atoms
• Resilient superconducting-element design with genetic algorithms
• Model Order Reduction for Open Quantum Systems Based on Measurement-adapted Time-coarse Graining
• Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations