learningQuantumNoiseFingerprint

Data and code for the paper: arXiv:2109.11405

The code and the data is available also on CodeOcean: https://codeocean.com/capsule/fa6e1d85-c99f-4a38-9c16-ac204da85040/

Learning the noise fingerprint of quantum devices

Abstract:

Noise sources unavoidably affect any quantum technological device. Noise's main features are expected to strictly depend on the physical platform on which the quantum device is realized, in the form of a distinguishable fingerprint. Noise sources are also expected to evolve and change over time. Here, we first identify and then characterize experimentally the noise fingerprint of IBM cloud-available quantum computers, by resorting to machine learning techniques designed to classify noise distributions using time-ordered sequences of measured outcome probabilities.

Instructions

• createCircuit.py is used to run in parallel the calls to the IBM framework to get the data that is stored in the folder data. In this repository you already find the retrieved data.
• extractExecutions.pyis used to process the memory saved in the previous step and get the outcome probabilities.
• createDataset.py and createDatasetTimeSeries.py are used to arrange the runs in classification datasets.
• conf.py and configurations.py define the configurations that are used during the training of the ML models.
• runSvm.py contains the main functions that implement the creation, training and optimization of the Support Vector Machine models.
• runSvmTable.py, runSvmTableHoriz.py and runSvmTableTriang.py are scripts that create the models, train them and create the latex tables to be used in the paper.
• calculateSlidingWindowTimeDat.ipynb is used to create the datapoints for the figure on sliding window.
• plotDates.ipynb is used to create the datapoints for the figure on slow dataset times.
• runTest.py is an example of execution of all the pipeline excluded the creation of the circuit (the latter is not necessary because the data is included in the repository).