Skip to main content

A extension of entropy lab for persistent storage of calibration parameters of a quantum processing unit (QPU).

Project description

Entropy QPU DB

Background

The entropy QPU DB is an extension designed to make it easy to calibrate and manage experimentation of quantum processing units. It provides two abilites:

  1. to run automated calibrations of all parameters related to the qubits, couplers and readout elements that make up a QPU, using calibration graphs, as inspired by Google's Optimus method.

  2. to store the calibration data in a persistent storage DB, and integrate that DB into the calibration framework.

One of the challenges of bringing up a QPU from "scratch" is that it's not always straightforward to understand which calibrations need to be, at what order and with which parameters. On the other hand, QPUs contain many parameters which require calibration and tracking, which makes automated tools essential for this task.

This means that the process of building the calibration graph for a QPU needs to be needs to be both flexible and powerful. The QPU DB is designed to allow to do just that.

Getting started

This package requires having entropy installed, which can be obtained from pipy here.

To get started, check out the tutorials under docs/.

Contact info

The QPU DB was conceived and developed by Lior Ella, Gal Winer, Ilan Mitnikov and Yonatan Cohen, and is maintained by Guy Kerem. For any questions, suggestions or otherwise - please contact us on our discord server!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

entropylab-qpudb-0.0.7.tar.gz (11.7 kB view details)

Uploaded Source

Built Distribution

entropylab_qpudb-0.0.7-py3-none-any.whl (12.3 kB view details)

Uploaded Python 3

File details

Details for the file entropylab-qpudb-0.0.7.tar.gz.

File metadata

  • Download URL: entropylab-qpudb-0.0.7.tar.gz
  • Upload date:
  • Size: 11.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.7.3 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.7.3

File hashes

Hashes for entropylab-qpudb-0.0.7.tar.gz
Algorithm Hash digest
SHA256 4aba2c3b67cd578c764ec83bfc9cbc50b39b419567a24ba02f523ae037d4aa5f
MD5 b3fb9ed0695822e1e12afdf5ab520ba2
BLAKE2b-256 9026d4109e95f8587afcc05adb9438dd1481cdc27a72736a4ad195d758860dc7

See more details on using hashes here.

File details

Details for the file entropylab_qpudb-0.0.7-py3-none-any.whl.

File metadata

  • Download URL: entropylab_qpudb-0.0.7-py3-none-any.whl
  • Upload date:
  • Size: 12.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.7.3 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.7.3

File hashes

Hashes for entropylab_qpudb-0.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 481124a30f35755653113f7e1776ad7e0da6276f550cf381d9ecb8a8a8441f27
MD5 43bebe40bae67a6fe01d4e0f3fdaee77
BLAKE2b-256 7249076640f2f95b3948f09280c43f4c445c38032008fe1c4098339da5d0ace6

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page