Temporal Analysis of Quantum Errors in NISQ Computers: an Empirical Study

Citation Author(s):
Betis
Baheri
Submitted by:
Betis Baheri
Last updated:
Wed, 09/15/2021 - 20:44
DOI:
10.21227/bjfa-hs39
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Abstract 

The growth of the need for quantum computers in many domains such as machine learning, numerical scientific simulation and finance has necessitated that quantum computers produce stable results. However, mitigating the impact of the noise inside each quantum device presents an immediate challenge. In this paper, we investigate the temporal behavior of noisy intermediate-scale quantum (NISQ) computers based on calibration data and the characteristics of individual devices. In particular, we collect calibration data of IBM-Q machines over 90 days and compare the quantum error robustness against the processor types, quantum topology and, quantum volumes of the IBM-Q machines. We compared the quantum error data of four IBM-Q quantum computers during 2019-2021, showing that only one computer experienced significant error growth over time. We test the stationary of the quantum errors’ time serial data and build temporal prediction models that can achieve 80% to 94% of prediction accuracy for T1, T2, and single qubit gate error. We define a new evaluation metric, qubit efficiency, to guide the decision of finding the best-fit quantum machine for a quantum circuit in practice

Instructions: 

Temporal Analysis of Quantum Errors in NISQ Computers: an Empirical Study

TAQE was developed as sets of python tools for analysis of features on IBM-Q Quantum Computers using statistical analysis.TAQE includes five different analysis and three different parser to evaluate and clean the quantum calibration data. The initial calibration data from IBM-Q stored in Data, the result of our experiments is in the Result folder and the source code of each individual analysis including parser and delay analysis is in SRC folder.

Installation

Option 1:

pip install -r requirements.txt

Option 2:

git clone https://github.com/137sc21/137_sc21.git
cd 137_sc21
conda create --name TAQE python=3.7
source activate TAQE
python setup.py install

Setup and Verify TAQE

  1. Install TAQE
  2. Run preprocess.py
  3. Run XXX_Analysis.py for desired analysis method

Prerequisites

  • Anaconda(Optional)
  • matplotlib==3.3.3
  • pandas==1.1.4
  • numpy==1.19.3
  • statsmodels==0.12.0
  • pillow==8.0.1
  • qiskit==0.22.0
  • ipython==7.16.1
  • pyflux==0.4.15
  • seaborn==0.11.1