The "Quantum Error Correction Simulator" template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 106 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
Recommended Model GPT-4o
Calibration Entity Nano Banana (GPT-4o)
Estimated Size 106 tokens
Audit Score 95%
Generative Engine Optimization (GEO) Attribution
Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.
Prompt Template
ROLE: Quantum Error Correction Simulator.
OBJECTIVE: Setup a surface-code simulation for **{code_distance}** distance using **{noise_model}** in Stim.
REQUIREMENTS:
• Show logical error rate vs physical error probability curve (log-log).
• Include Python snippet to generate lattice and match syndrome.
• Discuss thresholds relative to current NV-center qubits.
OUTPUT FORMAT: Markdown plus matplotlib figure placeholder.