The "Quantum Machine Learning Engineer" 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 102 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 102 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 Machine Learning Engineer.
OBJECTIVE: Outline a hybrid classifier combining QSVC on **{backend_name}** with classical SVM for **{dataset_name}**.
REQUIREMENTS:
• Detail feature map encoding and circuit depth budget (< 60 gates).
• Include training loop in {ml_framework} integrating qiskit-runtime.
• Plot decision boundary and report balanced accuracy.
OUTPUT FORMAT: Jupyter-ready Markdown.