autoqasm

v0.6.0 safe
3.0
Low Risk

Python-native programming library for developing quantum programs

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell execution, obfuscation, or credential harvesting. The metadata risk score is slightly elevated due to the author having only one package, but this alone does not indicate a supply-chain attack.

  • No network calls
  • No shell execution
  • Low obfuscation risk
  • No credential harvesting
  • Single package by author
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external services.
  • Shell: No shell execution detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package, which might indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • Test runner config found: setup.cfg
  • Test runner config found: conftest.py
  • 4 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • 2 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (10188 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 305 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 12 unique contributor(s) across 100 commits in amazon-braket/autoqasm
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository amazon-braket/autoqasm appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon Web Services" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with autoqasm 
Create a mini-application that allows users to write and simulate simple quantum circuits using the 'autoqasm' Python package. This application will serve as an educational tool for beginners interested in quantum computing. Here’s a detailed plan for the project:

1. **Project Overview**: The application will enable users to input their quantum circuit designs using a simplified Python interface provided by 'autoqasm'. It will then compile these circuits into QASM (Quantum Assembly Language) code and simulate their execution on a classical computer to visualize the outcomes.

2. **Features**:
   - User-friendly GUI for circuit design.
   - Support for basic quantum gates (Hadamard, Pauli-X, CNOT).
   - Visualization of quantum states before and after gate operations.
   - Simulation of circuit execution and display of measurement results.
   - Saving and loading of circuit designs.

3. **Using 'autoqasm'**:
   - Utilize 'autoqasm' to define and manipulate quantum registers and qubits.
   - Use 'autoqasm' functions to apply quantum gates to the defined qubits.
   - Leverage 'autoqasm' capabilities to generate QASM code from the quantum circuit definitions.
   - Integrate 'autoqasm' simulation tools to run the circuit simulations and analyze results.

4. **Implementation Steps**:
   - Step 1: Set up the development environment with Python and install necessary packages including 'autoqasm'.
   - Step 2: Design the GUI using a framework like Tkinter for Python.
   - Step 3: Implement the backend logic for defining and manipulating quantum circuits using 'autoqasm'.
   - Step 4: Develop the functionality to convert the user-defined circuits into QASM code.
   - Step 5: Implement the simulation of the circuits and display the results.
   - Step 6: Add features to save and load circuit designs.
   - Step 7: Test the application thoroughly to ensure all features work as expected.

5. **Additional Considerations**:
   - Ensure the application provides clear instructions and examples for new users.
   - Optimize the performance of the simulation to handle more complex circuits efficiently.
   - Plan for future enhancements such as adding more quantum gates and supporting multiple qubit systems.

💬 Discussion Feed

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