aiqbench analysis
This study reveals a critical vulnerability in current quantum computing education, where sophisticated autograding mechanisms fail to detect AI-generated solutions even with extensive personalization layers. The findings suggest that introductory Qiskit assignments are currently susceptible to generative AI tools, undermining the intended assessment of student understanding and engagement. Consequently, educational frameworks must evolve beyond simple deterrence strategies to address the reality that minimal prompting can bypass existing security checks in quantum homework systems.
Chatgpt Completed 150 Tested Quantum Tasks with Zero Resiliency

Every completed homework submission from 150 separate sessions successfully passed automated grading despite being generated by ChatGPT. These introductory quantum computing assignments, designed with personalization layers and hidden checks, offered zero resilience against even minimal prompting of readily available AI tools. This confirms current autograding methods are vulnerable to generative artificial intelligence.
Three assignment packages assess the impact of artificial intelligence on quantum computing education while encouraging active student engagement with results rather than prohibiting AI use. The assignments comprise seeded basis-state circuits featuring bit flips and customised measurement mappings; a Quantum Fourier Transform sequence followed by inverse transform recovery; and a seeded Deutsch-Jozsa algorithm utilising custom oracle masks. Deterrence layers were incorporated including deterministic personalisation, non-palindromic bitstrings, varied measurement maps, simulator execution, machine-readable JSON submissions, hidden References, circuit and transpiler metrics, reflections, and optional Quantum execution. Each package’s fixed student-visible instance was tested across 50 separate ChatGPT instances at Wilfrid Laurier University.…
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