This release is a pre-release and may not be stable for production use.
This package provides a library to define, train and deploy Quantum Machine Learning models.
This library has been used to train a qmodel with the Iris flower dataset on IBM quantum computers: iris.entropicalabs.io
The quantum circuits can run on top of any quantum computer provider. As for now, it implements interfaces for a fast simulator, manyq, and Qiskit.
Installation
From PyPI, at the command line:
pip install polyadicqml
Installing latest stable from github:
git clone https://github.com/entropicalabs/polyadicQML.git polyadicqml cd polyadicqml pip install -U .
Documentation
You can find a quickstart guide, the tutorial and the module references in the docs.
Sample code
Training a model on a simulator and testing it on a real quantum computer can be done in a few lines:
# Define the circuit structure
make_circuit(bdr, x, params):
...
# Prepare a circuit simulator:
qc = mqCircuitML(make_circuit=make_circuit,
nbqbits=nbqbits, nbparams=nbparams)
# Instanciate and train the model
model = Classifier(qc, bitstr).fit(input_train, target_train)
# Prepare to run the circuit on an IBMq machine:
backend = Backends("ibmq_ourense", hub="ibm-q")
qc2 = qkCircuitML(
make_circuit=make_circuit,
nbqbits=nbqbits, nbparams=nbparams,
backend=backend
)
# Change the model backend and run it
model.set_circuit(qc2)
model.nbshots = 300
model.job_size = 30
pred_test = model(input_test)
You can find out more in the documentation, where you will find tutorials and examples. A quickstart through examples can be found in the examples folder, as well as on the website. As an introduction to the algorithm you can check out this video presentation.
Metadata
Release files for polyadicqml 0.1.0b4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| polyadicqml-0.1.0b4.tar.gz | 19.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| polyadicqml-0.1.0b4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 46.5 kB
Release files / polyadicqml-0.1.0b4.tar.gz
| Download URL | polyadicqml-0.1.0b4.tar.gz |
|---|---|
| Size | 19.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
12f6e43514a13fadaa5d3c3b9454836eb70a4a2265c503c5faca8373ec6fa560
|
|
BLAKE2b-256 checksum How to use checksums |
816e1130a90e255da44fa994ba7343f48ee08e63b1510c648975d47f061c5594
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.8.2
|
Release files / polyadicqml-0.1.0b4-py3-none-any.whl
| Download URL | polyadicqml-0.1.0b4-py3-none-any.whl |
|---|---|
| Size | 26.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
632f03dd14dd8003f81b06fa3bb12b7487c94ae8ce9d8e59330b10f03522a62c
|
|
BLAKE2b-256 checksum How to use checksums |
a2755f19e27330a9141002277a0b5fae784b8bbe2fd03387aeb69e748d5eb2bf
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.8.2
|
Security for polyadicqml 0.1.0b4
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.