https://github.com/chigwell/quantum-spec-parser
A new package designed to extract and structure key technical specifications from quantum computing research and development texts. The package takes unstructured text input describing quantum process
https://github.com/chigwell/quantum-spec-parser
benchmarking data-consistency developers engineers fidelity-ranges llmatch-messages-integration material-composition quantum-computing quantum-hardware-advancement qubit-count research-development specification-extraction structured-output unstructured-data-parsing
Last synced: 11 days ago
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A new package designed to extract and structure key technical specifications from quantum computing research and development texts. The package takes unstructured text input describing quantum process
- Host: GitHub
- URL: https://github.com/chigwell/quantum-spec-parser
- Owner: chigwell
- Created: 2025-12-21T13:16:20.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-12-21T13:16:29.000Z (about 1 month ago)
- Last Synced: 2025-12-23T04:44:58.568Z (about 1 month ago)
- Topics: benchmarking, data-consistency, developers, engineers, fidelity-ranges, llmatch-messages-integration, material-composition, quantum-computing, quantum-hardware-advancement, qubit-count, research-development, specification-extraction, structured-output, unstructured-data-parsing
- Language: Python
- Homepage: https://pypi.org/project/quantum-spec-parser/
- Size: 3.91 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# quantum-spec-parser
[](https://badge.fury.io/py/quantum-spec-parser)
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/project/quantum-spec-parser)
[](https://www.linkedin.com/in/eugene-evstafev-716669181/)
**quantum-spec-parser** is a lightweight Python package that extracts and structures key technical specifications from unstructured quantum computing texts. It parses descriptions of quantum processors and returns a standardized list containing essential details such as qubit count, material composition, and fidelity ranges.
The package relies on the **llmatch‑messages** library for robust pattern matching and uses **ChatLLM7** (from `langchain_llm7`) as the default language model. You can also supply any LangChain‑compatible LLM of your choice.
---
## 📦 Installation
```bash
pip install quantum_spec_parser
```
---
## 🚀 Quick Start
```python
from quantum_spec_parser import quantum_spec_parser
# Example unstructured description of a quantum processor
user_input = """
The new Q-42 chip features 56 superconducting transmon qubits fabricated on a silicon substrate.
Gate fidelity lies between 99.2% and 99.7%, and the coherence time averages 120 µs.
"""
# Simple call – uses the default ChatLLM7 internally
specs = quantum_spec_parser(user_input)
print(specs)
# → ['56 qubits', 'silicon substrate', 'fidelity 99.2%‑99.7%', 'coherence time 120 µs']
```
---
## 📚 Detailed Usage
### Function Signature
```python
def quantum_spec_parser(
user_input: str,
api_key: Optional[str] = None,
llm: Optional[BaseChatModel] = None,
) -> List[str]:
```
| Parameter | Type | Description |
|-------------|--------------------------|-------------|
| `user_input`| `str` | The raw text containing quantum processor specifications. |
| `api_key` | `Optional[str]` | Your LLM7 API key. If omitted, the function reads `LLM7_API_KEY` from the environment or falls back to a placeholder. |
| `llm` | `Optional[BaseChatModel]`| A LangChain LLM instance to use instead of the default `ChatLLM7`. Any model that implements `BaseChatModel` works. |
If `llm` is **not** provided, the function automatically creates a `ChatLLM7` instance using the supplied (or env‑provided) API key.
---
### Using a Custom LLM
You can replace the default **ChatLLM7** with any LangChain‑compatible model, such as OpenAI, Anthropic, or Google Generative AI.
#### OpenAI
```python
from langchain_openai import ChatOpenAI
from quantum_spec_parser import quantum_spec_parser
llm = ChatOpenAI(model="gpt-4o-mini")
response = quantum_spec_parser(user_input, llm=llm)
```
#### Anthropic
```python
from langchain_anthropic import ChatAnthropic
from quantum_spec_parser import quantum_spec_parser
llm = ChatAnthropic(model="claude-3-haiku-20240307")
response = quantum_spec_parser(user_input, llm=llm)
```
#### Google Generative AI
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from quantum_spec_parser import quantum_spec_parser
llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
response = quantum_spec_parser(user_input, llm=llm)
```
---
### Supplying Your Own LLM7 API Key
The free tier of LLM7 usually suffices, but for higher rate limits you can provide a personal key:
```python
response = quantum_spec_parser(user_input, api_key="your-llm7-api-key")
```
Or set it globally via the environment:
```bash
export LLM7_API_KEY="your-llm7-api-key"
```
You can obtain a free key by registering at .
---
## 🛠️ Under the Hood
1. **Prompt construction** – The package builds system and human prompts defined in `prompts.py`.
2. **Pattern matching** – A regular expression (`pattern`) is compiled and passed to `llmatch` from `llmatch_messages`.
3. **LLM call** – The selected LLM generates a response.
4. **Extraction** – `llmatch` validates the response against the regex and returns the extracted data as a list of strings.
If the LLM call fails or the response does not match the pattern, a `RuntimeError` is raised with the underlying error message.
---
## 🐞 Issues & Contributions
If you encounter bugs or have feature requests, please open an issue:
Pull requests are welcome! Feel free to fork the repository and submit your improvements.
---
## ✍️ Author
**Eugene Evstafev** –
GitHub: [chigwell](https://github.com/chigwell)
---
## 📜 License
This project is licensed under the MIT License. See the `LICENSE` file for details.