Automated quantum system modeling with machine learning

TL;DR

This paper demonstrates that machine learning can automatically construct simplified models of complex quantum systems from basic measurements, accurately identifying key states and dynamics for systems with up to five effective states.

Contribution

It introduces a neural network approach that detects the number of effective states and relevant Hamiltonian terms from minimal quantum measurement data.

Findings

Neural networks can identify effective states and dynamics with ~10% error for systems with up to five states.

The method requires only simple measurement data, making it accessible for unknown quantum systems.

Potential for automated, initial modeling of quantum devices to complement traditional physics-based methods.

Abstract

Despite the complexity of quantum systems in the real world, models with just a few effective many-body states often suffice to describe their quantum dynamics, provided decoherence is accounted for. We show that a machine learning algorithm is able to construct such models, given a straightforward set of quantum dynamics measurements. The effective Hilbert space can be a black box, with variations of the coupling to just one accessible output state being sufficient to generate the required training data. We demonstrate through simulations of a Markovian open quantum system that a neural network can automatically detect the number $N $ of effective states and the most relevant Hamiltonian terms and state-dephasing processes and rates. For systems with $N\leq5$ we find typical mean relative errors of predictions in the $10 \%$ range. With more advanced networks and larger training sets, it is conceivable that a future single software can provide the automated first stop solution to model building for an unknown device or system, complementing and validating the conventional approach based on physical insight into the system.