extend the Qiskit classroom applications.
Project description
qiskit-classroom-converter
Qiskit classroom Converter
0.5.0 (2024-03-06) : The Version has been updated to be compatible with Qiskit 1.0.0 and Python 3.12.
Documents
https://kmu-quantum-classroom.github.io/qiskit-classroom-converter/qiskit_class_converter.html
Official USER Guide
https://kmu-quantum-classroom.github.io/documents/overview.html
Support convert method
- quantum circuit to bra-ket notation
- quantum circuit to matrix
- matrix to quantum circuit
- string to bra-ket notation
Options
convert method | option |
---|---|
QC_TO_BRA_KET | expression{simplify, expand}, print{raw} |
QC_TO_MATRIX | print{raw} |
MATRIX_TO_QC | label{str} |
STR_TO_BRA_KET | print{raw} |
from qiskit_class_converter import ConversionService
ConversionService(conversion_type="QC_TO_BRA_KET", option={"expression": "simplify"})
Required data
- MATRIX_TO_QC
- User's QuantumCircuit object
from qiskit import QuantumCircuit
from qiskit_class_converter import ConversionService
input_value = [
[1, 0, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 0]
]
sample_converter = ConversionService(conversion_type="MATRIX_TO_QC")
result = sample_converter.convert(input_value=input_value)
# using user's QuantumCircuit object
quantum_circuit = QuantumCircuit(2, 2)
quantum_circuit.append(result, [0, 1])
How to Install
pip install qiskit-classroom-converter
Docker Pull & Run
Alternative installation with docker image.
docker pull ghcr.io/kmu-quantum-classroom/qiskit-classroom-converter
docker run -p 8888:8888 ghcr.io/kmu-quantum-classroom/qiskit-classroom-converter
Dependencies
- qiskit
Usage
from qiskit import QuantumCircuit
from qiskit_class_converter import ConversionService
# quantum circuit to matrix
quantum_circuit = QuantumCircuit(2, 2)
quantum_circuit.x(0)
quantum_circuit.cx(0, 1)
sample_converter = ConversionService(conversion_type="QC_TO_MATRIX")
result = sample_converter.convert(input_value=quantum_circuit)
code : example.py
How to test the software
python -m unittest -v
or
tox
ARM Platform
Mac ARM chips users may have issues running this package.
We have provided a Dockerfile, which can be used docker-compose.
docker-compose up --build
Acknowledgement
- 국문 : "본 연구는 2022년 과학기술정보통신부 및 정보통신기획평가원의 SW중심대학사업의 연구결과로 수행되었음"(2022-0-00964)
- English : "This research was supported by the MIST(Ministry of Science, ICT), Korea, under the National Program for Excellence in SW), supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation) in 2022"(2022-0-00964)
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