qsteed

Name	qsteed JSON
Version	0.1.1 JSON
	download
home_page	https://github.com/BAQIS-Quantum/QSteed
Summary	Quantum Software of Compilation for supporting Real Quantum device
upload_time	2024-07-13 11:58:02
maintainer	None
docs_url	None
author	Xuhongze
requires_python	>=3.10
license	Apache-2.0 License
keywords	qsteed quantum compiling quantum computing quantum operating system
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # QSteed

[![License](https://img.shields.io/github/license/BAQIS-Quantum/qsteed.svg?)](https://opensource.org/licenses/Apache-2.0)
[![Current Release](https://img.shields.io/github/release/BAQIS-Quantum/qsteed.svg?)](https://github.com/BAQIS-Quantum/qsteed/releases)
![Python versions](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)
[![Downloads](https://img.shields.io/pypi/dm/qsteed.svg?)](https://pypi.org/project/qsteed/)

[//]: # (![PyPI - Python Version](https://img.shields.io/pypi/pyversions/qsteed))


**QSteed** is a **Q**uantum **S**of**t**war**e** of 
Compilation for supporting R**e**al Quantum **d**evice, 
including a quantum compiler, a quantum resource 
virtualization manager, and a task scheduler.

## Installation
### Install from PyPI
You can install QSteed via pip:
```bash
pip install qsteed
```

### Install from source
You can directly download the [source code](https://github.com/BAQIS-Quantum/qsteed/archive/refs/heads/master.zip) from GitHub
or clone the repository using the following command.
```bash
git clone https://github.com/BAQIS-Quantum/QSteed.git
```
Change to the qsteed directory install using the following command.
```bash
pip install -r requirements.txt
python setup.py install
```

## Example
```python
import matplotlib.pyplot as plt
from qsteed import Transpiler
from qsteed.backends.backend import Backend
from qsteed.passes.mapping.layout.sabre_layout import SabreLayout
from qsteed.passes.model import Model
from qsteed.passes.optimization.optimization_combine import GateCombineOptimization
from qsteed.passes.optimization.one_qubit_optimization import OneQubitGateOptimization
from qsteed.passes.unroll.unroll_to_2qubit import UnrollTo2Qubit
from qsteed.passes.unroll.unroll_to_basis import UnrollToBasis
from qsteed.utils.random_circuit import RandomCircuit
from qsteed.passflow.passflow import PassFlow

# Generating random quantum circuits (needs to be a pyquafu QuantumCircuit class)
rqc = RandomCircuit(num_qubit=5, gates_number=100, gates_list=['cx', 'rx', 'rz', 'ry', 'h'])
qc = rqc.random_circuit()

# Set chip information (the number of chip qubits needs to be consistent
# with the number of quantum circuit qubits)
basis_gates = ['cx', 'rx', 'ry', 'rz', 'id', 'h']
c_list = [(2, 3, 0.982), (3, 2, 0.982), (3, 4, 0.973), (4, 3, 0.973), 
          (0, 1, 0.98), (1, 0, 0.98), (1, 2, 0.97), (2, 1, 0.97)]
backend_properties = {
    'name': 'ExampleBackend',
    'backend_type': 'superconducting',
    'qubits_num': 5,
    'coupling_list': c_list,
    'basis_gates': basis_gates,
}

# Predefined compilation passflow
passes = [
    UnrollTo2Qubit(),
    SabreLayout(heuristic='fidelity', max_iterations=3),  # heuristic='distance' or 'fidelity', 'mixture'
    UnrollToBasis(basis_gates=basis_gates),
    GateCombineOptimization(),
    OneQubitGateOptimization()
]
passflow = PassFlow(passes=passes)

backend_instance = Backend(**backend_properties)
initial_model = Model(backend=backend_instance)

compiler = Transpiler(passflow, initial_model)
compiled_circuit = compiler.transpile(qc)
compiled_circuit.plot_circuit()
plt.show()
```

You can also use preset compilation passflow with optimization_level 0-3:
Using preset compilation passflow, see [preset_passflow.py](qsteed/passflow/preset_passflow.py)
```python
compiler = Transpiler(initial_model=initial_model)
compiled_circuit = compiler.transpile(qc, optimization_level=3)
```

## More Tests
See [tests](tests) for more examples.

## Real machine deployment
QSteed has been successfully deployed to the [Quafu Quantum Cloud Computing Cluster](https://quafu.baqis.ac.cn/), 
supporting the compilation and optimization of user tasks into quantum circuits 
executable by quantum processors. 
You can submit tasks through the [Quafu Composer](https://quafu.baqis.ac.cn/#/composer) web interface,
[Pyquafu](https://scq-cloud.github.io/) client, 
or [QuarkStudio](https://www.yuque.com/wuming-6g8w2/ghzgfk/azika5xqlpxig3q3?singleDoc#) client.

The public version of QSteed does not yet provide features such as 
quantum resource management and unified software-hardware interface calls. 
We will gradually make these features available according to our plan. 
If you would like to learn more about real machine deployment, please contact us.


## How to contribute
For information on how to contribute, please send an e-mail to members of developer of this project.

QSteed was developed by the quantum operating system team of the Beijing Academy of Quantum Information Sciences.

## License
QSteed is released under the Apache 2.0 license. See [LICENSE](LICENSE) for more details.

            

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    "description": "# QSteed\n\n[![License](https://img.shields.io/github/license/BAQIS-Quantum/qsteed.svg?)](https://opensource.org/licenses/Apache-2.0)\n[![Current Release](https://img.shields.io/github/release/BAQIS-Quantum/qsteed.svg?)](https://github.com/BAQIS-Quantum/qsteed/releases)\n![Python versions](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)\n[![Downloads](https://img.shields.io/pypi/dm/qsteed.svg?)](https://pypi.org/project/qsteed/)\n\n[//]: # (![PyPI - Python Version](https://img.shields.io/pypi/pyversions/qsteed))\n\n\n**QSteed** is a **Q**uantum **S**of**t**war**e** of \nCompilation for supporting R**e**al Quantum **d**evice, \nincluding a quantum compiler, a quantum resource \nvirtualization manager, and a task scheduler.\n\n## Installation\n### Install from PyPI\nYou can install QSteed via pip:\n```bash\npip install qsteed\n```\n\n### Install from source\nYou can directly download the [source code](https://github.com/BAQIS-Quantum/qsteed/archive/refs/heads/master.zip) from GitHub\nor clone the repository using the following command.\n```bash\ngit clone https://github.com/BAQIS-Quantum/QSteed.git\n```\nChange to the qsteed directory install using the following command.\n```bash\npip install -r requirements.txt\npython setup.py install\n```\n\n## Example\n```python\nimport matplotlib.pyplot as plt\nfrom qsteed import Transpiler\nfrom qsteed.backends.backend import Backend\nfrom qsteed.passes.mapping.layout.sabre_layout import SabreLayout\nfrom qsteed.passes.model import Model\nfrom qsteed.passes.optimization.optimization_combine import GateCombineOptimization\nfrom qsteed.passes.optimization.one_qubit_optimization import OneQubitGateOptimization\nfrom qsteed.passes.unroll.unroll_to_2qubit import UnrollTo2Qubit\nfrom qsteed.passes.unroll.unroll_to_basis import UnrollToBasis\nfrom qsteed.utils.random_circuit import RandomCircuit\nfrom qsteed.passflow.passflow import PassFlow\n\n# Generating random quantum circuits (needs to be a pyquafu QuantumCircuit class)\nrqc = RandomCircuit(num_qubit=5, gates_number=100, gates_list=['cx', 'rx', 'rz', 'ry', 'h'])\nqc = rqc.random_circuit()\n\n# Set chip information (the number of chip qubits needs to be consistent\n# with the number of quantum circuit qubits)\nbasis_gates = ['cx', 'rx', 'ry', 'rz', 'id', 'h']\nc_list = [(2, 3, 0.982), (3, 2, 0.982), (3, 4, 0.973), (4, 3, 0.973), \n          (0, 1, 0.98), (1, 0, 0.98), (1, 2, 0.97), (2, 1, 0.97)]\nbackend_properties = {\n    'name': 'ExampleBackend',\n    'backend_type': 'superconducting',\n    'qubits_num': 5,\n    'coupling_list': c_list,\n    'basis_gates': basis_gates,\n}\n\n# Predefined compilation passflow\npasses = [\n    UnrollTo2Qubit(),\n    SabreLayout(heuristic='fidelity', max_iterations=3),  # heuristic='distance' or 'fidelity', 'mixture'\n    UnrollToBasis(basis_gates=basis_gates),\n    GateCombineOptimization(),\n    OneQubitGateOptimization()\n]\npassflow = PassFlow(passes=passes)\n\nbackend_instance = Backend(**backend_properties)\ninitial_model = Model(backend=backend_instance)\n\ncompiler = Transpiler(passflow, initial_model)\ncompiled_circuit = compiler.transpile(qc)\ncompiled_circuit.plot_circuit()\nplt.show()\n```\n\nYou can also use preset compilation passflow with optimization_level 0-3:\nUsing preset compilation passflow, see [preset_passflow.py](qsteed/passflow/preset_passflow.py)\n```python\ncompiler = Transpiler(initial_model=initial_model)\ncompiled_circuit = compiler.transpile(qc, optimization_level=3)\n```\n\n## More Tests\nSee [tests](tests) for more examples.\n\n## Real machine deployment\nQSteed has been successfully deployed to the [Quafu Quantum Cloud Computing Cluster](https://quafu.baqis.ac.cn/), \nsupporting the compilation and optimization of user tasks into quantum circuits \nexecutable by quantum processors. \nYou can submit tasks through the [Quafu Composer](https://quafu.baqis.ac.cn/#/composer) web interface,\n[Pyquafu](https://scq-cloud.github.io/) client, \nor [QuarkStudio](https://www.yuque.com/wuming-6g8w2/ghzgfk/azika5xqlpxig3q3?singleDoc#) client.\n\nThe public version of QSteed does not yet provide features such as \nquantum resource management and unified software-hardware interface calls. \nWe will gradually make these features available according to our plan. \nIf you would like to learn more about real machine deployment, please contact us.\n\n\n## How to contribute\nFor information on how to contribute, please send an e-mail to members of developer of this project.\n\nQSteed was developed by the quantum operating system team of the Beijing Academy of Quantum Information Sciences.\n\n## License\nQSteed is released under the Apache 2.0 license. See [LICENSE](LICENSE) for more details.\n",
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