This release is a pre-release and may not be stable for production use.
TensorFlow Quantum is an open source library for high performance batch quantum computation on quantum simulators and quantum computers. The goal of TensorFlow Quantum is to help researchers develop a deeper understanding of quantum data and quantum systems via hybrid models.
TensorFlow Quantum was created in an ongoing collaboration between the University of Waterloo and the Quantum AI team at Google along with help from many other contributors within Google.
2 maintainers
Metadata
2 maintainers
Release files for tfq-nightly 0.7.3.dev20231011
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| tfq_nightly-0.7.3.dev20231011-cp39-cp39-macosx_12_1_x86_64.whl | CPython 3.9 | CPython 3.9 | macosx 12 1 x86 64 | Details for tfq_nightly-0.7.3.dev20231011-cp39-cp39-macosx_12_1_x86_64.whl |
Release files / tfq_nightly-0.7.3.dev20231011-cp39-cp39-macosx_12_1_x86_64.whl
| Download URL | tfq_nightly-0.7.3.dev20231011-cp39-cp39-macosx_12_1_x86_64.whl |
|---|---|
| Size | 8.5 MB |
| Tags | CPython 3.9 macosx 12 1 x86 64 |
|
SHA-256 checksum How to use checksums |
128fdcbad94737dcb564f5926abdedef0ddb6323496a10454920d007e3fbbb78
|
|
BLAKE2b-256 checksum How to use checksums |
73eff70788213a207449002820ed2fa9c77ab88f70d66a7afe84a6e8fe3a35de
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.6
|
Security for tfq-nightly 0.7.3.dev20231011
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
