ℏ hbar
A quantum leap for deep learning.
Quantum-inspired methods for neural models — compression, pruning, and operator-level surgery, built on matrix product operators (MPO) and MixT.
ħ = h/2π — the reduced Planck constant, the quantum of action. Smallest action. Biggest leap.
Install
pip install hbar
Status
🚧 v1.0.0 is a placeholder on PyPI — the implementation is landing next.
Why "hbar"?
Every quantum state lives in a Hilbert space. Almost every model lives in a smaller one — hbar finds it.
And ħ is the reduced Planck constant: reduction is where we start, not where we stop.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for hbar 1.0.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| hbar-1.0.1.tar.gz | 2.5 kB | Without provenance | Details for hbar-1.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| hbar-1.0.1-py3-none-any.whl | Python 3 | none | any | Details for hbar-1.0.1-py3-none-any.whl |
Total release size: 5.1 kB
Release files / hbar-1.0.1.tar.gz
| Download URL | hbar-1.0.1.tar.gz |
|---|---|
| Size | 2.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.12
|
Release files / hbar-1.0.1-py3-none-any.whl
| Download URL | hbar-1.0.1-py3-none-any.whl |
|---|---|
| Size | 2.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.12
|
Security for hbar 1.0.1
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No provenance found
None of the files in this release were uploaded with provenance metadata.
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