Quantum Markovianity as a supervised learning task

Abstract

Supervised learning algorithms take as input a set of labelled examples and return as output a predictive model. Such models are used to estimate labels for future, previously unseen examples drawn from the same generating distribution. In this paper we investigate the possibility of using supervised learning to estimate the dimension of a non-Markovian quantum environment. Our approach uses an ensemble learning method, the Random Forest Regressor, applied to classically simulated data sets. Our results indicate this is a promising line of research.

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• Estimating the degree of non-Markovianity using machine learning
• Probing non-Markovian quantum dynamics with data-driven analysis: Beyond "black-box" machine learning models
• Non-Markovian Memory Strength Bounds Quantum Process Recoverability
• Reassessing thermodynamic advantage from indefinite causal order
• On the sampling complexity of open quantum systems
• Memory Effects in Quantum Processes
• Quantum Process Identification: A Method for Characterizing Non-Markovian Quantum Dynamics
• Non-Markovian memory in IBMQX4
• Quantum reinforcement learning in the presence of thermal dissipation
• Hamiltonian characterisation of multi-time processes with classical memory
• Multi-time quantum process tomography on a superconducting qubit
• A de Finetti theorem for quantum causal structures
• Estimating the degree of non-Markovianity using variational quantum circuits
• Exploring fixed points and eigenstates of quantum systems with reinforcement learning