Supermarq

v0.5.66 safe
4.0
Medium Risk

Supermarq is a scalable, application-centric quantum benchmarking suite.

🤖 AI Analysis

Final verdict: SAFE

The package Supermarq v0.5.66 has minimal risks associated with it, showing no signs of network calls, shell execution, or obfuscation. However, the metadata and maintainer history indicate low effort, which slightly increases the risk score.

  • No network calls detected
  • No shell execution patterns
  • Low effort in metadata and maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low effort in metadata and maintainer history, but lacks clear malicious indicators.

🔬 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

Email domain looks legitimate: infleqtion.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Infleqtion/client-superstaq appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with Supermarq 
Create a quantum computing benchmarking tool using the 'Supermarq' package in Python. This tool will allow users to simulate and compare the performance of different quantum algorithms on various quantum simulators and real quantum hardware. Here’s a detailed breakdown of what your project should include:

1. **User Interface**: Develop a simple yet effective command-line interface (CLI) where users can input their quantum circuits and specify which backend they want to use for simulation or execution.
2. **Quantum Circuit Simulation**: Integrate Supermarq to simulate quantum circuits on local simulators like Qiskit Aer or Cirq Simulators. Users should be able to choose between different types of noise models for more realistic simulations.
3. **Benchmarking Features**: Implement benchmarking capabilities using Supermarq’s core functionalities. This includes measuring key performance indicators such as gate fidelity, circuit depth, runtime, and error rates.
4. **Comparison Tool**: Allow users to compare the performance of their quantum circuits across multiple backends. Provide visual outputs (charts/graphs) showing the differences in performance metrics.
5. **Customization Options**: Enable customization options within the CLI, allowing users to adjust parameters like number of shots, measurement repetitions, and noise levels.
6. **Documentation and Help**: Include comprehensive documentation and help guides within the CLI to assist new users in understanding how to use the tool effectively.

Use Supermarq to handle the heavy lifting of quantum circuit simulation and benchmarking, while focusing on building an intuitive user experience and insightful performance comparison tools.

💬 Discussion Feed

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