Machine Learning meets Quantum Foundations: A Brief Survey

Abstract

The goal of machine learning is to facilitate a computer to execute a specific task without explicit instruction by an external party. Quantum foundations seeks to explain the conceptual and mathematical edifice of quantum theory. Recently, ideas from machine learning have successfully been applied to different problems in quantum foundations. Here, we compile the representative works done so far at the interface of machine learning and quantum foundations. We conclude the survey with potential future directions.

arXiv comment17 pages, 10 figures

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Cited by in corpus (22)

• Noisy intermediate-scale quantum (NISQ) algorithms
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Learning Quantum Systems
• Review: Deep Learning in Electron Microscopy
• Computer-inspired Quantum Experiments
• Prospects of Quantum Computing for Molecular Sciences
• Noisy intermediate-scale quantum algorithm for semidefinite programming
• Learning quantum dynamics with latent neural ODEs
• Digital Discovery of 100 diverse Quantum Experiments with PyTheus
• Deep reinforcement learning for universal quantum state preparation via dynamic pulse control
• Quantifying Unknown Quantum Entanglement via a Hybrid Quantum-Classical Machine Learning Framework
• Dynamical learning of a photonics quantum-state engineering process
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• Mixed State Entanglement Classification using Artificial Neural Networks
• Persistent currents in ultracold gases
• Improving application performance with biased distributions of quantum states
• Data-Centric Machine Learning in Quantum Information Science
• Classification and reconstruction of optical quantum states with deep neural networks
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• No-go theorems for photon state transformations in quantum linear optics
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