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Demonstration of Quantum Speedup for Bernstein-Vazirani Algorithm

data

Please visit the following Dropbox link (https://www.dropbox.com/sh/jkcun8d842w9p4e/AAAHDhB5j-8qlTv4TltMoZrpa?dl=) to access the data. Once you have cloned the GitHub repo, please add the files in the folder above to the data/ folder.

Note that the data files are too large to be pushed back to GitHub, so please add data/ to your .gitignore file.

Circuits and calibration are in the data/ folder. Read data/readme_data.ipynb about how the data is organized and how to extract relevant information.

results

Raw values for the plotted results are in this folder.

plots

All the plots are in this folder

analysis

data is converted to results and plots here. Suggested order of files:

  • manipulating_bitstring.py: functions to analyze rawdata given as a dictionary of bitcounts

  • simulation.py: simulation of ideal circuits

  • rawdata_to_rawdf.py: convert rawdata to rawdf (pandas DataFrame)

  • ps_from_rawdf.py: compute success probabilities

  • tts_from_rawdf.py: compute TTS

  • bv-6_output_distribution.ipynb: Plotting output distributions

  • circuit_duration_from_calibration_data.py: calculate circuit duration

  • circuit_durations.ipynb: exemplifty circuit_duration_from_calibration_data.py

  • tts_calculation_without_bootstrapping.ipynb: calculating TTS from rawdf

  • bootstrapping.py: bootstrapping TTS data in order to compute error bars

  • tts_calculation_with_bootstrapping.ipynb: calculating TTS from rawdf with bootstrapping

  • generate_bootstrapped_linear_fits.nb: compute fits for TTS incorporating bootstrapping

  • plots_with_fits.nb: generate plots for TTS and lambda