DeepSeek R1 and Codestral Performance on Qiskit Quantum Code Generation Benchmarks

This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does the performance of Deepseek R1 and Codestral compare on Qiskit-based quantum code generation tasks when evaluated using the Qiskit HumanEval benchmark with varying levels of quantum circuit. As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.4/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the performance of Deepseek R1 and Codestral compare on Qiskit-based quantum code generation tasks when evaluated using the Qiskit HumanEval benchmark with varying levels of quantum circuit complexity?

Autonomous literature synthesis. Automated review score: 8.4/10. Full text and citation available at Assignee Research.