Noise fingerprints in quantum computers: Machine learning software tools
arXiv:2202.04581 · doi:10.1016/j.simpa.2022.100260
Abstract
In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum noise sources affecting a quantum device, as a quantum computer. Specifically, the software architecture is designed to classify successfully (more than 99% of accuracy) the noise fingerprints in different quantum devices with similar technical specifications, or distinct time-dependences of a noise fingerprint in single quantum machines.
arXiv comment9 pages, 2 figures
References in corpus (21)
- Scikit-learn: Machine Learning in Python
- Deep Learning in Neural Networks: An Overview
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Photonic quantum technologies
- Integrated Photonic Quantum Technologies
- Strong quantum computational advantage using a superconducting quantum processor
- Boson Sampling on a Photonic Chip
- Validating quantum computers using randomized model circuits
- Benchmarking an 11-qubit quantum computer
- A compact ion-trap quantum computing demonstrator
- Rydberg atom quantum technologies
- Quantum certification and benchmarking
- Quantum teleportation on a photonic chip
- Quantum information processing with trapped ions
- Superconducting Qubits: A Short Review
- Machine learning non-Markovian quantum dynamics
- Using deep learning to understand and mitigate the qubit noise environment
- Beyond Quantum Noise Spectroscopy: modelling and mitigating noise with quantum feature engineering
- Estimating the degree of non-Markovianity using machine learning
- Learning the noise fingerprint of quantum devices