Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics

TL;DR

This paper introduces a memory-enhanced quantum extreme learning machine that improves the characterization of non-Markovian quantum dynamics by leveraging temporal information and environmental memory effects.

Contribution

The study demonstrates that incorporating temporal memory into quantum learning models significantly improves the estimation of non-Markovian quantum dynamics, highlighting the role of environmental memory as a resource.

Findings

Temporal extensions improve estimation accuracy

Memory effects become more beneficial with stronger non-Markovianity

Memory incorporation yields robust and substantial improvements

Abstract

We use a Quantum Extreme Learning Machine for characterizing and estimating parameters of quantum dynamics generated by a tunable collision model. The input to the learning protocol consists of quantum states produced by successive system environment interactions, while the reservoir is implemented as a disordered many body quantum system evolving under a fixed Hamiltonian. We systematically explore how extending the QELM feature space, through the inclusion of temporal information and additional observables, affects estimation performance. Our results demonstrate that temporal extensions of the feature vector consistently and significantly enhance estimation accuracy relative to the baseline protocol. Notably, incorporating memory from earlier time steps yields the most substantial and robust improvements, whereas extensions based solely on additional observables offer only marginal gains. Crucially, the advantage conferred by temporal memory becomes increasingly pronounced as the dynamics become more strongly non Markovian, indicating that environmental memory effects serve as a constructive resource for learning.