| Name | hdh JSON |
| Version |
0.1.3
JSON |
| download |
| home_page | None |
| Summary | Hybrid Dependency Hypergraphs for quantum computation: translation, visualization, and partitioning. |
| upload_time | 2025-08-06 14:51:27 |
| maintainer | None |
| docs_url | None |
| author | Maria Gragera Garces |
| requires_python | <4.0,>=3.10 |
| license | MIT |
| keywords |
quantum
compiler
partitioning
hdh
hypergraph
|
| VCS |
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No requirements were recorded.
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|
# Hybrid Dependency Hypergraphs for Quantum Computation
<p style="text-align:center">
<a href="https://pypi.org/project/hdh/">
<img src="https://badge.fury.io/py/hdh.svg" alt="PyPI version">
</a>
· MIT Licensed ·
<a href="https://unitary.foundation">
<img src="https://img.shields.io/badge/Supported%20By-UNITARY%20FOUNDATION-brightgreen.svg?style=for-the-badge" alt="Unitary Foundation">
</a>
· Author: Maria Gragera Garces
<br><br>
<em>Work in Progress - Preparing for 1.0</em>
</p>
---
## What is HDH?
**HDH (Hybrid Dependency Hypergraph)** is an intermediate representation designed to describe quantum computations in a model-agnostic way.
It provides a unified structure that makes it easier to:
- Translate quantum programs (e.g., from Qiskit or QASM) into a common hypergraph format
- Analyze and visualize the logical and temporal dependencies within a computation
- Partition workloads across devices using tools like METIS or KaHyPar, taking into account hardware and network constraints
---
## Current Capabilities
- Qiskit circuit translation
- OpenQASM 2.0 file parsing
- Graph-based printing and canonical formatting
- Partitioning with METIS using custom HDH-to-graph translation
- Model-specific abstractions for:
- Quantum Circuits
- Measurement-Based Quantum Computing (MBQC)
- Quantum Walks
- Quantum Cellular Automata (QCA)
Includes test examples for:
- Circuit translation (`test_convert_from_qiskit.py`)
- QASM import (`test_convert_from_qasm.py`)
- MBQC (`mbqc_test.py`)
- Quantum Walks (`qw_test.py`)
- Quantum Cellular Automata (`qca_test.py`)
- Protocol demos (`teleportation_protocol_logo.py`)
---
## Installation
```bash
pip install hdh
```
---
## Quickstart
### From Qiskit
```python
from qiskit import QuantumCircuit
from hdh.converters.convert_from_qiskit import from_qiskit
from hdh.visualize import plot_hdh
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
hdh = from_qiskit(qc)
plot_hdh(hdh)
```
### From QASM file
```python
from hdh.converters.convert_from_qasm import from_qasm
from hdh.visualize import plot_hdh
qasm_path = os.path.join(os.path.dirname(__file__), 'test_qasm_file.qasm')
hdh = from_qasm('file', qasm_path)
plot_hdh(hdh)
```
---
## Example Use Cases
- Visualize quantum protocols (e.g., teleportation)
- Analyze dependencies in quantum walk evolutions
- Explore entanglement flow in MBQC patterns
---
## Coming Soon
- Compatibility with Cirq, Braket, and Pennylane
- Full graphical UI for HDH visualization
- Native noise-aware binning strategies
- Analysis tools for:
- Cut cost estimation across partitions
- Partition size reporting
- Parallelism tracking by time step
```python
from hdh.passes.cut import compute_cut, cost, partition_sizes, compute_parallelism_by_time
num_parts = 3
partitions = compute_cut(hdh, num_parts)
print(f"\nMETIS partition into {num_parts} parts:")
for i, part in enumerate(partitions):
print(f"Partition {i}: {sorted(part)}")
# plot_hdh(hdh)
cut_cost = cost(hdh, partitions)
sizes = partition_sizes(partitions)
global_parallelism = compute_parallelism_by_time(hdh, partitions, mode="global")
parallelism_at_t3 = compute_parallelism_by_time(hdh, partitions, mode="local", time_step=3)
print("\n--- QW Metrics ---")
print(f"\nCut cost: {cut_cost}")
print(f"Partition sizes: {sizes}")
print(f"Parallelism over time: {global_parallelism}")
print(f"Parallelism at time t=3: {parallelism_at_t3}")
```
---
## Tests and Demos
All tests are under `tests/` and can be run with:
```bash
pytest
```
If you're interested in the HDH of a specific model, see in manual_tests:
- `mbqc_test.py` for MBQC circuits
- `qca_test.py` for Cellular Automata
- `qw_test.py` for Quantum Walks
- `teleportation_protocol_logo.py` for a protocol-specific demo
---
## Contributing
Pull requests welcome. Please open an issue or get in touch if you're interested in:
- SDK compatibility
- Optimization strategies
- Frontend tools (visualization, benchmarking)
---
## Citation
More formal citation and paper preprint coming soon. Stay tuned for updates.
Raw data
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"description": "\n\n\n# Hybrid Dependency Hypergraphs for Quantum Computation\n\n<p style=\"text-align:center\">\n <a href=\"https://pypi.org/project/hdh/\">\n <img src=\"https://badge.fury.io/py/hdh.svg\" alt=\"PyPI version\">\n </a>\n \u00b7 MIT Licensed \u00b7\n <a href=\"https://unitary.foundation\">\n <img src=\"https://img.shields.io/badge/Supported%20By-UNITARY%20FOUNDATION-brightgreen.svg?style=for-the-badge\" alt=\"Unitary Foundation\">\n </a>\n \u00b7 Author: Maria Gragera Garces\n <br><br>\n <em>Work in Progress - Preparing for 1.0</em>\n</p>\n\n---\n\n## What is HDH?\n\n**HDH (Hybrid Dependency Hypergraph)** is an intermediate representation designed to describe quantum computations in a model-agnostic way.\nIt provides a unified structure that makes it easier to:\n\n- Translate quantum programs (e.g., from Qiskit or QASM) into a common hypergraph format\n- Analyze and visualize the logical and temporal dependencies within a computation\n- Partition workloads across devices using tools like METIS or KaHyPar, taking into account hardware and network constraints\n\n---\n\n## Current Capabilities\n\n- Qiskit circuit translation \n- OpenQASM 2.0 file parsing \n- Graph-based printing and canonical formatting \n- Partitioning with METIS using custom HDH-to-graph translation \n- Model-specific abstractions for:\n - Quantum Circuits\n - Measurement-Based Quantum Computing (MBQC)\n - Quantum Walks\n - Quantum Cellular Automata (QCA)\n\nIncludes test examples for:\n\n- Circuit translation (`test_convert_from_qiskit.py`)\n- QASM import (`test_convert_from_qasm.py`)\n- MBQC (`mbqc_test.py`)\n- Quantum Walks (`qw_test.py`)\n- Quantum Cellular Automata (`qca_test.py`)\n- Protocol demos (`teleportation_protocol_logo.py`)\n\n---\n\n## Installation\n\n```bash\npip install hdh\n```\n\n---\n\n## Quickstart\n\n### From Qiskit\n\n```python\nfrom qiskit import QuantumCircuit\nfrom hdh.converters.convert_from_qiskit import from_qiskit\nfrom hdh.visualize import plot_hdh\n\nqc = QuantumCircuit(2)\nqc.h(0)\nqc.cx(0, 1)\n\nhdh = from_qiskit(qc)\n\nplot_hdh(hdh)\n```\n\n### From QASM file\n\n```python\nfrom hdh.converters.convert_from_qasm import from_qasm\nfrom hdh.visualize import plot_hdh\n\nqasm_path = os.path.join(os.path.dirname(__file__), 'test_qasm_file.qasm')\nhdh = from_qasm('file', qasm_path)\n\nplot_hdh(hdh)\n```\n\n---\n\n## Example Use Cases\n\n- Visualize quantum protocols (e.g., teleportation) \n- Analyze dependencies in quantum walk evolutions \n- Explore entanglement flow in MBQC patterns \n\n---\n\n## Coming Soon\n\n- Compatibility with Cirq, Braket, and Pennylane \n- Full graphical UI for HDH visualization \n- Native noise-aware binning strategies \n- Analysis tools for:\n - Cut cost estimation across partitions\n - Partition size reporting\n - Parallelism tracking by time step\n \n ```python\n from hdh.passes.cut import compute_cut, cost, partition_sizes, compute_parallelism_by_time\n\n num_parts = 3\n partitions = compute_cut(hdh, num_parts)\n\n print(f\"\\nMETIS partition into {num_parts} parts:\")\n for i, part in enumerate(partitions):\n print(f\"Partition {i}: {sorted(part)}\")\n \n # plot_hdh(hdh)\n cut_cost = cost(hdh, partitions)\n sizes = partition_sizes(partitions)\n global_parallelism = compute_parallelism_by_time(hdh, partitions, mode=\"global\")\n parallelism_at_t3 = compute_parallelism_by_time(hdh, partitions, mode=\"local\", time_step=3)\n\n print(\"\\n--- QW Metrics ---\")\n print(f\"\\nCut cost: {cut_cost}\")\n print(f\"Partition sizes: {sizes}\")\n print(f\"Parallelism over time: {global_parallelism}\")\n print(f\"Parallelism at time t=3: {parallelism_at_t3}\")\n\n ```\n\n---\n\n## Tests and Demos\n\nAll tests are under `tests/` and can be run with:\n\n```bash\npytest\n```\n\nIf you're interested in the HDH of a specific model, see in manual_tests:\n\n- `mbqc_test.py` for MBQC circuits \n- `qca_test.py` for Cellular Automata \n- `qw_test.py` for Quantum Walks \n- `teleportation_protocol_logo.py` for a protocol-specific demo \n\n---\n\n## Contributing\n\nPull requests welcome. Please open an issue or get in touch if you're interested in:\n\n- SDK compatibility \n- Optimization strategies \n- Frontend tools (visualization, benchmarking) \n\n---\n\n## Citation\n\nMore formal citation and paper preprint coming soon. Stay tuned for updates.\n\n",
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