Work

Detecting Markovianity of quantum processes via recurrent neural networks

Year: 2025

Type: article

Abstract: We present a novel methodology utilizing recurrent neural networks (RNNs) to classify Markovian and non-Markovian quantum processes, leveraging time series data derived from Choi states. The model exhibits exceptional accuracy, surpassing 95%, across diverse scenarios, including dephasing and Pauli channels in an arbitrary basis, generalized amplitude damping dynamics, and even in the presence of noise. Additionally, the developed model shows efficient forecasting capabilities for the analyzed time series data. These results suggest the potential of RNNs in discerning and predicting the Markovian nature of quantum processes.

Source: Quantum Machine Intelligence

DOI: 10.1007/s42484-025-00252-w

Authors: Angela Rosy Morgillo, Massimiliano F. Sacchi, Chiara Macchiavello

Institutions: University of Pavia, Istituto Nazionale di Fisica Nucleare, Sezione di Pavia, Istituto di Fotonica e Nanotecnologie

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,390

Funder: Università degli Studi di Pavia