semvec/pypi-packages huggingface dataset:
qio 0.1.31
Common quantum computing objects description and conversion library
License: Apache-2.0
Requires Python: >=3.10

<p align="center"><img width="50%" src="docs/logo.png" /></p>

# qio
`qio` is a python package to smoothly manipulate quantum computing objects between client and server.

It handle all the conversion boilerplate to focus your dev on your quantum SDK and backend on real matters.

## Installation

We encourage installing `qio` via the pip tool (a Python package manager):

```bash
pip install qio
```

## Getting started

To leverage `qio` and get quick interoperability between your frontend and your backend, you simply need to use qio wrappers system.

Here a snippet code for `cirq` and `qsim`:

```python
import cirq

from cirq.circuits import Circuit
from qsimcirq import QSimSimulator

from qio.core import (
    QuantumComputationModel,
    QuantumComputationParameters,
    QuantumProgramResult,
    QuantumProgram,
)

#############
# Client side

qc = _random_cirq_circuit(10)
shots = 100

program = QuantumProgram.from_cirq_circuit(qc)

model_json = QuantumComputationModel(
    programs=[program],
).to_json_str()

parameters_json = QuantumComputationParameters(
    shots=shots,
).to_json_str()

# Send this to server side

###########################
# Server / Computation side

model = QuantumComputationModel.from_json_str(model_json)
params = QuantumComputationParameters.from_json_str(parameters_json)

circuit = model.programs[0].to_cirq_circuit()

qsim_simulator = QSimSimulator()

qsim_result = qsim_simulator.run(circuit, repetitions=params.shots)

program_result = QuantumProgramResult.from_cirq_result(result).to_json_str()

# Send this to back to client side

#####################
# Back to client side

cirq_result = qresult.to_cirq_result()

qiskit_result = qresult.to_qiskit_result()
```
beekeeper-ai 1.0.17
Load any data in one line of code and connect with AI applications
License: Apache-2.0
Requires Python: <3.14,>=3.11
Keywords: AI, LLM, QA, RAG, data, observability, retrieval, semantic-search

# 🗂️ Beekeeper 🐝

![PyPI - Version](https://img.shields.io/pypi/v/beekeeper-ai)
![Python Version](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fbeekeeper-ai%2Fbeekeeper%2Frefs%2Fheads%2Fmain%2Fpyproject.toml&style=flat)
[![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch)
![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)
![Pepy Total Downloads](https://img.shields.io/pepy/dt/beekeeper-ai)
![PyPI - License](https://img.shields.io/pypi/l/beekeeper-ai)

## Installation 

```bash
pip install beekeeper-ai
```

## 📄 Documentation

The documentation can be found Click [here](https://beekeeper-ai.github.io/beekeeper/)!

Please check it out for overview of the interfaces, components, integrations and other resources!