{"id":24659207,"url":"https://github.com/carlolepelaars/skq","last_synced_at":"2026-05-19T19:39:27.527Z","repository":{"id":274188928,"uuid":"827827106","full_name":"CarloLepelaars/skq","owner":"CarloLepelaars","description":"Scientific Toolkit for Quantum Computing","archived":false,"fork":false,"pushed_at":"2025-03-01T20:33:19.000Z","size":1727,"stargazers_count":1,"open_issues_count":2,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-21T06:12:48.122Z","etag":null,"topics":["numpy","python","quantum","quantum-computing","quantum-machine-learning"],"latest_commit_sha":null,"homepage":"https://carlolepelaars.github.io/skq/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CarloLepelaars.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-12T13:11:02.000Z","updated_at":"2025-03-01T20:32:59.000Z","dependencies_parsed_at":"2025-02-27T17:18:16.337Z","dependency_job_id":"0b9dadd7-2fab-40b3-b130-f7466444dd1a","html_url":"https://github.com/CarloLepelaars/skq","commit_stats":null,"previous_names":["carlolepelaars/skq"],"tags_count":6,"template":false,"template_full_name":null,"purl":"pkg:github/CarloLepelaars/skq","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CarloLepelaars%2Fskq","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CarloLepelaars%2Fskq/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CarloLepelaars%2Fskq/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CarloLepelaars%2Fskq/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CarloLepelaars","download_url":"https://codeload.github.com/CarloLepelaars/skq/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CarloLepelaars%2Fskq/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33229393,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-19T15:49:41.270Z","status":"ssl_error","status_checked_at":"2026-05-19T15:49:22.917Z","response_time":58,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["numpy","python","quantum","quantum-computing","quantum-machine-learning"],"created_at":"2025-01-26T02:53:32.436Z","updated_at":"2026-05-19T19:39:22.511Z","avatar_url":"https://github.com/CarloLepelaars.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# skq\n\n![](https://img.shields.io/pypi/dm/skq)\n![Python Version](https://img.shields.io/badge/dynamic/toml?url=https://raw.githubusercontent.com/CarloLepelaars/skq/main/pyproject.toml\u0026query=%24.project%5B%22requires-python%22%5D\u0026label=python\u0026color=blue) \n[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)\n[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)\n\n\n\nScientific Toolkit for Quantum Computing\n\nThis library is used in the [q4p (Quantum Computing for Programmers)](https://github.com/CarloLepelaars/q4p) course.\n\n![q4p](https://carlo.ai/images/q4p.png)\n\nNOTE: 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 sure you check computations. \n\n## Why SKQ?\n\n- Exploration: Play with fundamental quantum building blocks using [NumPy](https://numpy.org).\n- Education: Learn quantum computing concepts and algorithms.\n- Integration: Combine classical components with quantum components.\n- 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.\n\n## Install\n\n```bash\npip install -U skq\n```\n\n## Quickstart\n\n### Circuit Conversion\n\nRun this code snippet to initialize Grover's algorithm and convert to Qiskit to run on quantum hardware. The algorithm can also be run within `skq` as a classical simulation.\n\n```python\nfrom skq.circuits import Grover\n\n# Initialize Grover's search skq Circuit\ncircuit = Grover().circuit(n_qubits=3, target_state=np.array([0, 0, 0, 0, 1, 0, 0, 0]), n_iterations=1)\n\n# Conversion to Qiskit\nqiskit_circuit = circuit.convert(framework=\"qiskit\")\nqiskit_circuit.draw()\n#      ┌───┐┌──────────────┐┌──────────────────┐┌─┐      \n# q_0: ┤ H ├┤0             ├┤0                 ├┤M├──────\n#      ├───┤│              ││                  │└╥┘┌─┐   \n# q_1: ┤ H ├┤1 PhaseOracle ├┤1 GroverDiffusion ├─╫─┤M├───\n#      ├───┤│              ││                  │ ║ └╥┘┌─┐\n# q_2: ┤ H ├┤2             ├┤2                 ├─╫──╫─┤M├\n#      └───┘└──────────────┘└──────────────────┘ ║  ║ └╥┘\n# c: 3/══════════════════════════════════════════╩══╩══╩═\n#                                                0  1  2 \n\n# Run circuit as classical simulation\nprint(grover([1,0,0,0,0,0,0,0]))\n# array([0.03125, 0.03125, 0.03125, 0.03125, 0.78125, 0.03125, 0.03125, 0.03125])\n```\n\n### Circuits from scratch\n\nYou can also build your own custom circuits from scratch using individual gates. All gates can be converted to popular frameworks like Qiskit and OpenQASM.\n\n```python\nfrom skq.gates import H, I, CX\nfrom skq.circuits import Concat, Circuit\n\nH() # Hadamard gate (NumPy array)\n# H([[ 0.70710678+0.j,  0.70710678+0.j],\n#    [ 0.70710678+0.j, -0.70710678+0.j]])\n\nI() # Identity gate (NumPy array)\n# I([[1.+0.j, 0.+0.j],\n#    [0.+0.j, 1.+0.j]])\n\nCX() # CNOT gate (NumPy array)\n# CX([[1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n#     [0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j],\n#     [0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j],\n#     [0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j]])\n\n# Initialize Bell State skq Circuit\ncircuit = Circuit([Concat([H(), I()]), CX()])\n\n# Simulate circuit classically\nstate = np.array([1, 0, 0, 0]) # |00\u003e state\ncircuit(state)\n# array([0.70710678+0.j, 0, 0, 0.70710678+0.j])\n\n# Conversion to Qiskit (Identity gates are removed)\nqiskit_circuit = circuit.convert(framework=\"qiskit\")\nqiskit_circuit.draw()\n#      ┌───┐     \n# q_0: ┤ H ├──■──\n#      └───┘┌─┴─┐\n# q_1: ─────┤ X ├\n#           └───┘\n\n# Conversion to OpenQASM\nqasm_circuit = circuit.convert(framework=\"qasm\")\nprint(qasm_circuit)\n# h q[0];\n# cx q[0], q[1];\n```\n\n![q4p](https://carlo.ai/images/q4p.png)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarlolepelaars%2Fskq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcarlolepelaars%2Fskq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarlolepelaars%2Fskq/lists"}