Quantum noise modeling through reinforcement learning
Abstract In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. In this work, we introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning (RL), offering increased flexibility in reproducing various noise models compared to conventional techniques such as randomized benchmarking or heuristic noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits. Additionally, we provide practical use-case examples for the study of renowned quantum algorithms.
Authors
- Simone Bordoni (ORCID: https://orcid.org/0000-0002-9745-9189)
- Alejandro Sopena (ORCID: https://orcid.org/0000-0002-9687-0099)
- Stefano Carrazza (ORCID: https://orcid.org/0000-0002-0079-6753)
- S. Giagu (ORCID: https://orcid.org/0000-0001-9192-3537)
- Andrea Papaluca (ORCID: https://orcid.org/0000-0003-4295-9749)
- Piergiorgio Buttarini
Institutions
- Australian National University (AU)
- University of Milan (IT)
- Technology Innovation Institute (AE)
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano (IT)
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma I (IT)
- Universidad Autónoma de Madrid (ES)
- European Organization for Nuclear Research (CH)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Quantum Science and Technology
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1088/2058-9565/ae1e98
- Citations
- 1
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- European Commission
- Ministerio de Ciencia e Innovación