Optional IBM Quantum hardware execution supplied extra evidence but did not remove substantial AI assistance, as ChatGPT generated the code and could interpret returned noisy data¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In exploratory QPE, QFT, and Deutsch–Jozsa runs on IBM backends, the theoretically expected bitstring remained dominant despite noise, but the workflow stayed LLM-assisted from code generation through interpretation. → Alexei Kaltchenko and Gurnivaj Tiwana 2026

Evidence¶

Alexei Kaltchenko and Gurnivaj Tiwana 2026¶

Alexei Kaltchenko and Gurnivaj Tiwana. (2026). ChatGPT Solves All Tested Qiskit Homework Assignments. arXiv. https://arxiv.org/abs/2608.19707 doi:10.48550/arxiv.2608.19707

q2 · i? · design · r2

Secondary exploratory evidence from earlier QPE, QFT, and Deutsch–Jozsa notebooks run on real devices, reported separately from the 150-session completion count. Table III shows expected counts such as 3709/4096 (90.55%) on ibm_kingston for QFT, with transpilation substantially increasing depth while results remained interpretable.

"It did not eliminate the minimally engaged workflow: ChatGPT generated the code, the operator ran it, and the resulting data could be returned for interpretation."

Discussion¶

Related Claims¶

• ChatGPT produced executed, grader-accepted submissions for all three fixed personalized Qiskit assignment instances in 150 of 150 sessions — related
• Reviewed literature reports risks of overreliance, AI errors in complex tasks, and students' uncritical acceptance of AI-generated outputs — a broader claim this one bears on
• Personalized ChatGPT-supported feedback in an augmented-reality quantum physics laboratory improved learning outcomes and directed visual attention — related
• In archived sessions, no operator code changes or quantum-logic corrections were needed; eight of nine notebook executions only required one permitted fix of a missing visualization dependency — related
• Module 3 code execution success dropped to approximately 60% before a missing-dependency infrastructure fix, recovering to match the 86–87% rates of Modules 1 and 2 — related