Skip to main content

Python module for interfacing with the CQC tket library of quantum software

Project description

Pytket is a python module for interfacing with TKET, an optimising compiler for quantum circuits developed by Cambridge Quantum. In addition to pytket there are several extension modules for accessing a range of quantum hardware and classical simulators. The extension modules also provide integration with several widely used quantum software tools.

The source code for the TKET compiler can be found in this github repository.

Installation

Installation is supported for Linux, MacOS and Windows. Installation requires python 3.8, 3.9 or 3.10.

To install run the pip command:

pip install pytket

See Installation troubleshooting for help with installation.

To install the pytket extension modules add a hyphen and the extension name to the command:

pip install pytket-quantinuum

For a list of pytket extensions see this page: https://cqcl.github.io/pytket-extensions/api/index.html.

Documentation and Examples

API reference: https://cqcl.github.io/tket/pytket/api/

To get started using pytket see the user manual.

For worked examples using TKET see our examples repository.

Support and Discussion

For bugs and feature requests we recommend creating an issue on the github repository.

User support: tket-support@cambridgequantum.com

For discussion, join the public slack channel here.

Mailing list: join here.

Citation

If you wish to cite TKET in any academic publications, we generally recommend citing our software overview paper for most cases.

If your work is on the topic of specific compilation tasks, it may be more appropriate to cite one of our other papers:

  • "On the qubit routing problem" for qubit placement (a.k.a. allocation) and routing (a.k.a. swap network insertion, connectivity solving). https://arxiv.org/abs/1902.08091 .
  • "Phase Gadget Synthesis for Shallow Circuits" for representing exponentiated Pauli operators in the ZX calculus and their circuit decompositions. https://arxiv.org/abs/1906.01734 .
  • "A Generic Compilation Strategy for the Unitary Coupled Cluster Ansatz" for sequencing of terms in Trotterisation and Pauli diagonalisation. https://arxiv.org/abs/2007.10515 .

Project details


Release history Release notifications | RSS feed

This version

1.6.0

Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

pytket-1.6.0-cp310-cp310-win_amd64.whl (10.5 MB view details)

Uploaded CPython 3.10 Windows x86-64

pytket-1.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pytket-1.6.0-cp310-cp310-macosx_11_0_arm64.whl (9.0 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pytket-1.6.0-cp310-cp310-macosx_10_14_x86_64.whl (10.0 MB view details)

Uploaded CPython 3.10 macOS 10.14+ x86-64

pytket-1.6.0-cp39-cp39-win_amd64.whl (10.4 MB view details)

Uploaded CPython 3.9 Windows x86-64

pytket-1.6.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pytket-1.6.0-cp39-cp39-macosx_11_0_arm64.whl (9.0 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pytket-1.6.0-cp39-cp39-macosx_10_14_x86_64.whl (10.0 MB view details)

Uploaded CPython 3.9 macOS 10.14+ x86-64

pytket-1.6.0-cp38-cp38-win_amd64.whl (10.5 MB view details)

Uploaded CPython 3.8 Windows x86-64

pytket-1.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

pytket-1.6.0-cp38-cp38-macosx_11_0_arm64.whl (9.0 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

pytket-1.6.0-cp38-cp38-macosx_10_14_x86_64.whl (10.0 MB view details)

Uploaded CPython 3.8 macOS 10.14+ x86-64

File details

Details for the file pytket-1.6.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pytket-1.6.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 10.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for pytket-1.6.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 c454f409beef088e20a87ea7d8e114cdc95350e4d75483612865d5182e6eefde
MD5 883422ff4102921472313d9e8e0b18ee
BLAKE2b-256 15815f312704645d81ccfb15cf2156ecb7a6be9e3d7b6ea361c562f9c3bf6862

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d46f98b041fa786ebd8b17f2c70f2265e1f8c4ff40360180a3fa9b8a06567de5
MD5 91121416b8a207ecb22a2e8017c34832
BLAKE2b-256 b3213e52bba7aa728c7371422a893d9f0603ffeb4e364ac999969a982ce0a40c

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 eabbd47493c600396769b9d73072d9784b88ad9e7e04e8f18a28de2d8f520d09
MD5 83b2a7466f91ed7de5088bc4b6b884e0
BLAKE2b-256 efc73fec27f54d953c03fc68ed43cf74fe03401ea8219323af412112f9674315

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp310-cp310-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp310-cp310-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 69c139495eb6d5b617d547a98c0a764f2f5b1a5a18a242ccf190e8609802f823
MD5 49e5a596200f6e1284e58605d40dc106
BLAKE2b-256 b2ec93d380817254445f751911d0bb3571a761a505b560f4fa45b9c76c38fc2e

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pytket-1.6.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 10.4 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for pytket-1.6.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 d898d3a779a973f0cdd7f530617abdd66958872e543aa9487c09cf181aa3eb67
MD5 4949aa3ff16267733d4c8a4972e92a39
BLAKE2b-256 49b6707f47b88d783ca0a85a43f3b899d17de04886d6241a66f578eaead78ac3

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 915d13e51771ef7ce98ddf0c9bdcc63b0aab3e706b7a6649e79dc36bb6413359
MD5 74561c00bd64ce07df1466059be6a363
BLAKE2b-256 05449ad69c8c39831dfd6fbee08ca697a30e32a4a0ffa5539a471f871ee9cd4a

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1fd8f5ca1ea1dfce9d7d9e4c7250183e295d691a387b920ba6b9e547f265b634
MD5 2bc8b7898e157c2946a6f64062d76cf6
BLAKE2b-256 0000d6c54bbef3699dea9a5f03afa279f7dbc91bbfe16ee9ed8471866cfbf3ae

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 e86871578990b9e94b0d2bbabcc1c502f971075ea2ce9c7892a119df84db1afc
MD5 c0352de609a84702ddd08b11f4556d8a
BLAKE2b-256 abbc5caf093ecee34bc4ed6740fe27f1d7971bdb44983f61ffb50c405b83c1f4

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pytket-1.6.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 10.5 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for pytket-1.6.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 59323947a8df1eb24951c5d3f3775f807193048f66e436f78d4104d72ac74f7a
MD5 ca106657ade3c9d217da7dade6a99815
BLAKE2b-256 ea4080d33c7b8ff2d9b1553ca42997aab4833025ca6643969407579f90fb1f07

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 149fe151169196b7dce2bc2b60caa34127b5275f74a239f0b5545d094e0d10f8
MD5 9130e2947d3a45cdd6356f400321435c
BLAKE2b-256 83a7fc1f89129821693cc990073c2be4dec7a84a096fd81664d1222036e2e060

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2d2d2ce5790ebd6d1b6bafbf3bbb85b6dd34db6cfed0f74090b76c6d9b90276f
MD5 c02ba2f79df866ac62937b9add20ceee
BLAKE2b-256 579bce4a745291a35d2fadd696bfc6974aed53b0b33c1b410176371c1035a272

See more details on using hashes here.

File details

Details for the file pytket-1.6.0-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for pytket-1.6.0-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 b700f95f4738aaee5f5975fe438613e3edf799b747b4a2ee01797523eebf8728
MD5 8c4fcd278fdd44c46fa2683331c7f3f3
BLAKE2b-256 271013f85bc2e04e48b4f3c159d065e4447de0bd60e790fbf69b6beba8899a0e

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