semvec/pypi-packages huggingface dataset:
squid-qml 21.3.1
SQUID: Hybrid Quantum-Classical ML package

# SQUID
Scaled QUantum IDentifer (SQUID) is a package that allows for seamless integration of quantum models into classical ones.

## Introduction

SQUID builds on top of PyTorch to integrate Quantum Machine Learning (QML) models into its classical framework.
The main goal of the project is to reduce amount of code needed to train QML algorithms on classical data.
However, some useful side-effects include:

* Load arbitrarily sized data, since encoder in front of quantum model can scale it down for use in near-term quantum devices
* Ability to easily compare performance of classical and quantum algorithms (By changing middle model of [MainModel](https://pysquid.readthedocs.io/en/latest/source/squid.models.html#main-model)).
This is especially useful in case of quantum-inspired classical algorithms.
* Easily evaluate impact of different encodings

More detailed documentation available [here](https://pysquid.readthedocs.io/).

## Installation

To install from pip:
```
pip install squid-qml
```

Alternatively one can follow instructions from [source document - DEVELOP readme](https://bitbucket.org/squid-qml/squid/src/master/DEVELOP.md).