AI Analysis
Final verdict: SAFE
The package presents a low risk profile with no signs of obfuscation or credential harvesting. The metadata suggests a potentially new or less active maintainer but does not indicate any malicious intent.
- No obfuscation detected
- No credential harvesting patterns
- Single package from the maintainer
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The maintainer has only one package and lacks PyPI classifiers, indicating potential low effort or new account status.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 7 test file(s) found
7 test file(s) detected (e.g. test_core_engines.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (8398 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
125 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "Varun Daiya, Yusuf Tahir" 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 ampamp
Create a Python-based mini-application that leverages the 'ampamp' package to demonstrate the practical use of Quantum Amplitude Amplification (QAA), Quantum Singular Value Transformation (QSVT), and Variational Training of Amplitude Amplification (VTAA). Your application should be user-friendly, allowing users to input parameters such as the number of qubits, the initial state vector, and the target amplitude to perform these quantum operations. Step 1: Begin by setting up your development environment. Ensure you have Python installed, along with the 'ampamp' package and any other necessary dependencies. Step 2: Design a simple graphical user interface (GUI) using a library like Tkinter. This GUI will allow users to select which quantum operation they wish to perform (QAA, QSVT, or VTAA). Step 3: Implement the core functionalities of each quantum operation within your application. Utilize the 'ampamp' package to handle the complex mathematical and computational aspects of these operations. For instance, when a user selects QAA, your application should use 'ampamp' to amplify the amplitude of specific states in a given quantum system. Step 4: Add a feature where users can visualize the results of their chosen operation. Use libraries such as Matplotlib to plot the probability distribution of the final quantum state after the operation has been applied. Step 5: Enhance the user experience by adding error handling and informative messages. Ensure that the application gracefully handles incorrect inputs and provides clear instructions on how to use it effectively. Suggested Features: - Allow users to customize the initial state vector of the quantum system. - Provide options for different types of input states (e.g., uniform superposition, single-qubit states). - Include a brief explanation of each quantum operation and its potential applications in fields like cryptography and optimization problems. - Enable users to save the results of their computations to a file. - Offer an option to simulate the quantum operations on classical hardware before running them on actual quantum computers.
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