Unraveling Quantum Environments: Transformer-Assisted Learning in Lindblad Dynamics

TL;DR

This paper presents a Transformer-based machine learning framework that accurately infers time-dependent dissipation rates in open quantum systems from observable data, without prior knowledge of initial states or system Hamiltonians.

Contribution

It introduces a novel Transformer-based approach for learning dissipation profiles in Lindblad quantum dynamics using observable time series data, applicable to complex models.

Findings

Successfully reconstructs fixed and time-dependent decay rates.

Proves that dissipation rates are uniquely determined by finite observables.

Demonstrates effectiveness on various quantum models, including multi-qubit and light-matter systems.

Abstract

Understanding dissipation in open quantum systems is crucial for the development of robust quantum technologies. In this work, we introduce a Transformer-based machine learning framework to infer time-dependent dissipation rates in quantum systems governed by the Lindblad master equation. Our approach uses time series of observable quantities, such as expectation values of single Pauli operators, as input to learn dissipation profiles without requiring knowledge of the initial quantum state or even the system Hamiltonian. We demonstrate the effectiveness of our approach on a hierarchy of open quantum models of increasing complexity, including single-qubit systems with time-independent or time-dependent jump rates, two-qubit interacting systems (e.g., Heisenberg and transverse Ising models), and the Jaynes--Cummings model involving light--matter interaction and cavity loss with time-dependent decay rates. Our method accurately reconstructs both fixed and time-dependent decay rates from observable time series. To support this, we prove that under reasonable assumptions, the jump rates in all these models are uniquely determined by a finite set of observables, such as qubit and photon measurements. In practice, we combine Transformer-based architectures with lightweight feature extraction techniques to efficiently learn these dynamics. Our results suggest that modern machine learning tools can serve as scalable and data-driven alternatives for identifying unknown environments in open quantum systems.