Machine learning for quantum information and computing

Abstract: This compilation thesis explores the merger of machine learning, quantum information, and computing. Inspired by the successes of neural networks and gradient-based learning, the thesis explores how such ideas can be adapted to tackle complex problems that arise during the modeling and control of quantum systems, such as quantum tomography with noisy data or optimizing quantum operations, by incorporating physics-based constraints. We also discuss the Bayesian estimation of a quantum state with uncertainty estimates using physically meaningful priors. Classical machine learning could inspire new quantum-computing algorithms. One such idea is presented to extend the capabilities of variational quantum algorithms using implicit differentiation, enabling straightforward computation of physically interesting quantities on a quantum computer as a gradient. Implicit differentiation also leads to a novel method to generate multipartite entangled quantum states and allows hyperparameter tuning of quantum machine learning algorithms. Several new experiments were possible due to the theoretical and numerical techniques developed in the thesis — robust generation of a Gottesman- Kitaev-Preskill and cubic phase state in a 3D cavity, fast process tomography of a new family of superconducting gates with known noise, efficient process tomography of a physical operation implementing a logical gate on a bosonic error-correction code, and the reconstruction of a photoelectron’s quantum state.