Scientific Toolkit for Quantum Computing
Project description
skq
Scientific Toolkit for Quantum Computing
This library is used in the q4p (Quantum Computing for Programmers) course.
NOTE: This library is developed for educational purposes. While we strive for correctness of everything, the code is provided as is and not guaranteed to be bug-free. For sensitive applications make to check computations.
Why SKQ?
- Exploration: Play with fundamental quantum building blocks (NumPy).
- Education: Learn quantum computing concepts and algorithms.
- Integration: Combine classical components with quantum components.
- Democratize quantum for Python programmers and data scientists: Develop quantum algorithms in your favorite environment and easily export to your favorite quantum computing platform for running on real quantum hardware.
Install
pip install skq
The default skq installation contains conversion to qiskit. PennyLane support can be installed as an optional dependency.
All backends
pip install skq[all]
PennyLane
pip install skq[pennylane]
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file skq-0.1.6.tar.gz.
File metadata
- Download URL: skq-0.1.6.tar.gz
- Upload date:
- Size: 30.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.9.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4769253edf2ca7871e1fbdd515d2a17f6f701d940edde47b8f85700294b6db59
|
|
| MD5 |
ff331f861750c8fe42c84b6c87460c5b
|
|
| BLAKE2b-256 |
7026dc68c303e9c10f66a07bfc32587941bda25b1441d7c25f223e37ffd64aab
|
File details
Details for the file skq-0.1.6-py3-none-any.whl.
File metadata
- Download URL: skq-0.1.6-py3-none-any.whl
- Upload date:
- Size: 36.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.9.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f08b9827ecdbadbf73321a01a6f0dc067a69cf1e29483b08aa77a57138ffcdda
|
|
| MD5 |
4fd12a1f7eb4709e2c1479266f3c7245
|
|
| BLAKE2b-256 |
12c3c7d22a85cf8022e610f4d121ca4a9336209102c8d4e23174c2b4ab8b77fb
|
