pdft
A Python port of ParametricDFT.jl: learning parametric quantum Fourier transforms via manifold optimization. The package implements a variational approach that approximates the Discrete Fourier Transform (DFT) with parameterized quantum circuits.
Status: early scaffold. The Julia package is the reference implementation; this repository will grow the Python equivalent incrementally.
Installation
Once published on PyPI:
pip install pdft
From source:
git clone https://github.com/zazabap/pdft.git
cd pdft
pip install -e ".[dev]"
Quick start
(coming soon — mirrors the make example demo in the upstream Julia
package)
Background
See the upstream notes for the theory:
License
MIT. See LICENSE. This project is a derivative port of ParametricDFT.jl (Copyright © 2025 nzy1997, MIT).
1 maintainer
Release files for pdft 0.2.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pdft-0.2.3.tar.gz | 144.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pdft-0.2.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 218.2 kB
Release files / pdft-0.2.3.tar.gz
| Download URL | pdft-0.2.3.tar.gz |
|---|---|
| Size | 144.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / pdft-0.2.3-py3-none-any.whl
| Download URL | pdft-0.2.3-py3-none-any.whl |
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| Size | 73.9 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.14
|
Provenance
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