PySTE

A Python package for evolving unitaries and states under the Schrödinger equation using first-order Suzuki-Trotter and computing switching functions.

PySTE stands for Python Suzuki-Trotter-Evolver and is a Python interface to the C++ header-only library Suzuki-Trotter-Evolver: https://github.com/Christopher-K-Long/Suzuki-Trotter-Evolver (doi:10.5281/zenodo.17116329).

While PySTE has limited functionality in comparison to more fleshed out quantum simulation packages such as QuTiP, it is faster at some tasks:

benchmark PySTE vs QuTiP

More detailed benchmarks can be found here: https://PySTE.readthedocs.io/en/latest/benchmarks/index.html

Installation

PySTE can be installed as follows:

pip install py-ste

However, the package should be imported as:

import py_ste

Support

Current support:

	macOS Intel (10.13+, 10.15+ on 3.14)	macOS Apple Silicon (11.0+)	Windows 64bit	Windows 32bit	Windows Arm64	manylinux musllinux x86_64	Other Linux
CPython 3.8	✅	✅	✅	✅	Build from source	✅	Build from source
CPython 3.9	✅	✅	✅	✅	Build from source	✅	Build from source
CPython 3.10	✅	✅	✅	✅	Build from source	✅	Build from source
CPython 3.11	✅	✅	✅	✅	✅	✅	Build from source
CPython 3.12	✅	✅	✅	✅	✅	✅	Build from source
CPython 3.13	✅	✅	✅	✅	✅	✅	Build from source
CPython 3.14	✅	✅	✅	✅	✅	✅	Build from source

Currently, the pre-built wheels only include the dynamic evolvers. For the faster static evolvers please build from source.

Requirements

Requires:

NumPy (>= 1.21, < 3)
Suzuki-Trotter-Evolver (== 1.1.2, packaged with PySTE)
Eigen3 (>=3.4, <6, packaged with PySTE)

Note that Suzuki-Trotter-Evolver and Eigen3 are packaged with PySTE and so do not need to be installed separately.

If on Linux and using a conda environment you may encounter an error

version `GLIBCXX_...' not found

to fix this you also need to execute:

conda install -c conda-forge libstdcxx-ng

Additional requirements for testing

toml
PyYAML
SciPy (== 1.*)

Additional requirements for benchmarking

Build from source

There are several flags that can be passed which will rebuild PySTE from source using various optimisations. The flags are:

--config-setting="cmake.define.NCTRL_FIXED_SIZES=RANGE"
Defines the strategy used to compile evolvers with a fixed number of controls. The options are:
- OFF: There will only be evolvers with a dynamic number of controls.
- SINGLE: There will only be a single evolver with a fixed number of controls specified by --config-setting="cmake.define.NCTRL=...".
- RANGE (default option): There will be evolvers with a fixed number of controls in the range $[1,$ MAX_NCTRL$]$ where MAX_NCTRL can be set with --config-setting="cmake.define.MAX_NCTRL=..."
- POWER: The evolvers with a fixed number of controls will have a number of controls given by powers in the range $[1,$ MAX_POWER_NCTRL$]$ of the base BASE_NCTRL where MAX_POWER_NCTRL can be set with --config-setting="cmake.define.MAX_POWER_NCTRL=..." and BASE_NCTRL with --config-setting="cmake.define.BASE_NCTRL=..."
--config-setting="cmake.define.NCTRL=1"
The number of controls when using --config-setting="cmake.define.NCTRL_FIXED_SIZES=SINGLE". A positive integer, by default 1.
--config-setting="cmake.define.MAX_NCTRL=14"
The maximum number of controls when using --config-setting="cmake.define.NCTRL_FIXED_SIZES=RANGE". A positive integer, by default 14.
--config-setting="cmake.define.MAX_POWER_NCTRL=3"
The maximum power when using --config-setting="cmake.define.NCTRL_FIXED_SIZES=POWER". A positive integer, by default 3.
--config-setting="cmake.define.BASE_NCTRL=2"
The base of the powers when using --config-setting="cmake.define.NCTRL_FIXED_SIZES=POWER". A positive integer, by default 2.
--config-setting="cmake.define.DIM_FIXED_SIZES=RANGE"
Defines the strategy used to compile evolvers with a fixed vector space dimension. The options are:
- OFF: There will only be evolvers with a dynamic vector space dimension.
- SINGLE: There will only be a single evolver with a fixed vector space dimension specified by --config-setting="cmake.define.DIM=...".
- RANGE (default option): There will be evolvers with a fixed vector space dimension in the range $[1,$ MAX_DIM$]$ where MAX_DIM can be set with --config-setting="cmake.define.MAX_DIM=..."
- POWER: The evolvers with a fixed vector space dimension will have a vector space dimension given by powers in the range $[1,$ MAX_POWER_DIM$]$ of the base BASE_DIM where MAX_POWER_DIM can be set with --config-setting="cmake.define.MAX_POWER_DIM=..." and BASE_DIM with --config-setting="cmake.define.BASE_DIM=..."
--config-setting="cmake.define.DIM=2"
The vector space dimension when using --config-setting="cmake.define.DIM_FIXED_SIZES=SINGLE". A positive integer, by default 2.
--config-setting="cmake.define.MAX_DIM=16"
The maximum vector space dimension when using --config-setting="cmake.define.DIM_FIXED_SIZES=RANGE". A positive integer, by default 16.
--config-setting="cmake.define.MAX_POWER_DIM=4"
The maximum power when using --config-setting="cmake.define.DIM_FIXED_SIZES=POWER". A positive integer, by default 4.
--config-setting="cmake.define.BASE_DIM=2"
The base of the powers when using --config-setting="cmake.define.DIM_FIXED_SIZES=POWER". A positive integer, by default 2.

For example,

pip install --no-binary py-ste py-ste \
--no-cache-dir \
--config-setting="cmake.define.NCTRL_FIXED_SIZES=SINGLE" \
--config-setting="cmake.define.NCTRL=2" \
--config-setting="cmake.define.DIM_FIXED_SIZES=POWER" \
--config-setting="cmake.define.MAX_POWER_DIM=3" \
--verbose

will build PySTE from source and optimises evolvers at compile time with 2 control Hamiltonians acting on a vector space of dimensions 2, 4, and 8. Increasing the number of optimised evolvers increases the compile time. The --no-binary py-ste flag instructs pip to build py-ste from source and the following py-ste instructs pip that py-ste is one of the packages to collect (indeed the only package). The --no-cache-dir flag ensures that a cached .whl file is not used instead and the package is actually built from source. Finally, the --verbose flag allows the progress of the build to be seen. This is useful as the builds can often take a long time.

Note building from source requires approximately 16GB of RAM.

Cloning the repository

PySTE uses git submodules and so should be cloned with the flag --recurse-submodules as follows:

git clone --recurse-submodules https://github.com/Christopher-K-Long/PySTE

Alternatively, you can clone the repository normally and then initialise the submodules as follows

git clone https://github.com/Christopher-K-Long/PySTE
git submodule update --init --recursive

Documentation

Documentation, including worked examples, can be found at: https://PySTE.readthedocs.io

Source Code

Source code can be found at: https://github.com/Christopher-K-Long/PySTE

A mirror can be found at: https://gitlab.com/Christopher-K-Long/PySTE

Please submit all pull requests, issues, discussions, and vulnerability reports to the GitHub repository.

Releases from this repository are assigned DOIs and can be found at https://doi.org/10.5281/zenodo.17116431. The DOI for all releases is 10.5281/zenodo.17116431. Additionally, the releases are archived to https://archive.softwareheritage.org/swh:1:dir:508bd76667d9f26712852bbdc31d87d44b72bfc5.

Version and Changes

The current version is 1.1.2. Please see the Change Log for more details. PySTE uses semantic versioning.

Acknowledgements

CKL would like to thank Chris Hall for useful discussions on the structure of the package.

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SHA256	`5af09c3164a19427911fb448f3c634c33dfe384f382dcf84493d17363539677c`
MD5	`b88473593cf5a9846c238214dad82713`
BLAKE2b-256	`aca3a16abbf4994c96338fb44e24e416552e1eca3d788a6c8b9472aac3fc761c`

Algorithm	Hash digest
SHA256	`710c175b268a4f9d1c5f8bf79e613440c9c552d61e2fce4da10e1258408ff7e0`
MD5	`de6e42ae7b8a15f808bb8022cec16de3`
BLAKE2b-256	`de7bff90701a8ce8db1514fbc28e88968edf2d384e4ddea05b31f29968d8cc89`

Algorithm	Hash digest
SHA256	`e9235c2ec6be68a1e4e8459617260f82fb08a0d849cfb7234be5b41e0e7ea92e`
MD5	`0bac8b193a8c1a864bd3af5ac6994c0c`
BLAKE2b-256	`17b6d61ef60914cd2b287076a4dc4269e4a19cfb39f2d0d15ddc3ab0da08ccd4`

Algorithm	Hash digest
SHA256	`c2e6dd24173d5f89bd755b1039c1d8441bc67f3c48a6268d30dc397187217dad`
MD5	`9d7190a42e8db8a4fde9f083238084b7`
BLAKE2b-256	`d31864038d2216c0a8c801936edd606fb561d3364e8f97ffad962ed8263ea79d`

Algorithm	Hash digest
SHA256	`6845be70bdf613825ed52beb249c2154db47280b2931faaba1b12c50c7496ff1`
MD5	`594147c1b48d5ecec0aa69367faada03`
BLAKE2b-256	`090b9ade8341f4a965f5ad167e3076bca409faf31258fa877fd04a5a49dbed01`

Algorithm	Hash digest
SHA256	`ede2ae2cccda3c51834f240cd8b22d18dde003ffdc6a82ac140c96d4ca42ba97`
MD5	`97e271dab265e069aca8bb82b228f4af`
BLAKE2b-256	`ca881b8a0492eb30f7b923faca4ca0e445564bb00ff234799ebe9a47a46f0c0a`

Algorithm	Hash digest
SHA256	`0786ed99c582cdea43f3d4dca8f2a20cce63181b7e9da2d8230bba3b3cdbcf06`
MD5	`663e77d70284f22de95004e7d71ce002`
BLAKE2b-256	`bd965f47a623c64918c51d8737be16fa832f380bb7cafdc52a0580ecf78f618c`

Algorithm	Hash digest
SHA256	`427b8f728669ecf7ebb65f0cc767d6c470f2a43fb7358f55f4897634b7462355`
MD5	`27c8157e75cb593b3a096d2bfb1904d3`
BLAKE2b-256	`52e5b67c2faeaf1efe92ee1690f4cebb0c5362a5b231b9fb34c4c5031ea71e75`

Algorithm	Hash digest
SHA256	`b1068101f70c853e2ab817e3c0af22da541a06b76b1c7091f816df3dc1c4ff73`
MD5	`caa3f3679baf51d789e8b2477e5de5e8`
BLAKE2b-256	`9305da5b88724312dc27fd791d8fcf43ebf35989d9d0e348c7625be1fd72a9e0`

Algorithm	Hash digest
SHA256	`19fd82a8a41fe40d119f940a65ff3a1f64459cc9716047af74ba6326de5c7404`
MD5	`eae14c6273b4105b5e5026af48304f2e`
BLAKE2b-256	`02a3c7e0ba096673b5cb7459202b7e65557865b93be7b2d0601a6d01ca4dca20`

Algorithm	Hash digest
SHA256	`322c23c4b171de882bf9f6d425ca693bfb1750100f3ae1c9cf5bdfba00b95089`
MD5	`28b6383a37ac5cfdad93eb77906469b3`
BLAKE2b-256	`07c2eb74a2e9382a16a61917d9deb1764426f9841d8670832013d7e6ceaad0ed`

Algorithm	Hash digest
SHA256	`e8201718024e72a3cc0d5573f773698b9b2f58d26061437ce80dc213fc8a8c6a`
MD5	`fbea0e4f5bfd2d6e82e3e8527e079f25`
BLAKE2b-256	`9575600c7ee725d9fe05205d680e746836617bc887eccdc9889b568df3864c43`

Algorithm	Hash digest
SHA256	`c0fbe7676ed5910227783f369c3ed4ee48392c69d3f5dce5247de2100cda87f4`
MD5	`279733356b8e802c8546bd0d2ba876a2`
BLAKE2b-256	`8e8e4916fae8cd44612bdb432682405231f38fef04eb940538e3f1d5bc017a5a`

Algorithm	Hash digest
SHA256	`ee1ed41429c404362f64661ac34e7abcef28e977459ba12b15653ae5ed01bdc9`
MD5	`e6f441e0d9a091328747c22eaec73a1c`
BLAKE2b-256	`721651908e420055a3f3c4619e55aaebc1ee3bca8c633e1dbc4e40380038f3c6`

Algorithm	Hash digest
SHA256	`095873bd69c1e29ddc985e370a2efcfde529e8d92bd060b884b81420c591c807`
MD5	`a5a8f885ead3d55fede1ea0279647af5`
BLAKE2b-256	`0fc8c412ec96f0d4866674c446d2af33e0587867b56ad67e300cabe72cd958b4`

Algorithm	Hash digest
SHA256	`9cce2feaa96eae81be832cb1c3491c243093db8b09944cb257d34e2757e0d715`
MD5	`c77e25cb6bf245cfdcbb3035b4f21dec`
BLAKE2b-256	`b17f725c6125fe04a2cf983d97023147184b69ecb021420e2ee7fc0d8ce2a6e3`

py-ste 1.1.2

PySTE

Installation

Support

Requirements

Additional requirements for testing

Additional requirements for benchmarking

Build from source

Cloning the repository

Documentation

Source Code

Version and Changes

Acknowledgements

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License expression

Requires

Tags

Classifiers

Download files

Source Distribution

Built Distributions

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File hashes

Algorithm	Hash digest
SHA256	`c198a5cd9a910f359ddb7ad382f21f701cddf804c3a7bdc29a6b60cb213f9a6e`
MD5	`f6369390ae33255668d6fe005b81bdf4`
BLAKE2b-256	`acd4067e7068b9b258cc6b1ac70f16b8ac927f3273ddc404e47ebec0ef1725e0`

Algorithm	Hash digest
SHA256	`ca0d5332eb499db5b138d13da2193ca94e9dcef5ec947fc5c5d87ec07ab401c7`
MD5	`f65865f19a06acb63acb25345d9b51f1`
BLAKE2b-256	`28517cacca9ba249d49e1a47d80d6991e111faa1b63d4f11109a347b4a656068`

Algorithm	Hash digest
SHA256	`5c72d1166fc71d522541f751fef5cf80ec2c2b97163aa489b78413383f11842d`
MD5	`57476e71742938dfce7e10e4709761a0`
BLAKE2b-256	`ddc00fd3a95e0d8796dbec1726d4331980a9cae03486cc6d796f824a859b4013`

Algorithm	Hash digest
SHA256	`cfeb7b2b3b287c1cc8ca22ecb9f322e6e1df454d7e6adc393800b40d1f1ab6c3`
MD5	`6ee3f3c3d155ec1b32316067a632c759`
BLAKE2b-256	`b7533358f067a00f4c7c61dce19c47246dd554dec5cbce8934cd2acc1a88d7d5`

Algorithm	Hash digest
SHA256	`1fc5dc52075d3c769daf04462fbf3f02e579aa5bc27ba7b63a76d7d017aea466`
MD5	`dbb4b462499e117d4639d0e909d98938`
BLAKE2b-256	`d80f850dcc70cacbb5a22d1534b8aa2e35acb2ad1a6a1c4a67c9a769bd76793b`

Algorithm	Hash digest
SHA256	`78bc3e75e607d49ac5b17e10eb1f9fbc852f2e4fef7390de0829f391130dbb54`
MD5	`1a7bce740b665a24b0e3747acff1c7fc`
BLAKE2b-256	`9943312db033fafdbeac29a26bc6a49a0fdd09011aa078211d126c7c360a4f3d`

Algorithm	Hash digest
SHA256	`9bbdfab2c2af157a60cda8111ac4169995260de1ae8141a0508634cf9ffc22cd`
MD5	`13907b09dcad7a45738b31155bcb0610`
BLAKE2b-256	`8cf8530c42cb96b92d1ceb696abcb9360052f00ed7886024484dfa579e834461`

Algorithm	Hash digest
SHA256	`a20b0cb2a3cb05035eff1e21abb119a4e71a4bd94e64cc7cefc46739a35c2673`
MD5	`c9d08266754804df6bf66499029039e4`
BLAKE2b-256	`010cd33bca6bcfd07d45612013ab9486dd1221387d0aacac3f46b3e0c79f873b`

Algorithm	Hash digest
SHA256	`acaac362d6981de52d2905a1d48f76865f807533f69afc45f4f487d8fee9568a`
MD5	`a6c256d7c3721b66d0b2d42dd0e25c7e`
BLAKE2b-256	`20206c16d7fecbf005d908db860b49e893c81eaf1d801cebda317b8505336b8c`

Algorithm	Hash digest
SHA256	`59289f37f315b1f815ff413b03db2948b9dde8113026b7bc2e35814c5a422996`
MD5	`717d1802ee9ebb9ea1daaf692a5ed265`
BLAKE2b-256	`368c138c7f004f05dc349c5ec2ba688c8f982e1e3335235d068af758d04ec527`