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data-encoder

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Classical data encoding skill for quantum machine learning applications

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/domains/science/quantum-computing/skills/data-encoder/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/data-encoder/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

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Data Encoder

Purpose

Provides expert guidance on encoding classical data into quantum states for machine learning applications, balancing expressiveness with circuit complexity.

Capabilities

  • Angle encoding
  • Amplitude encoding
  • IQP encoding
  • Hardware-efficient encoding
  • Encoding expressibility analysis
  • Data re-uploading strategies
  • Feature scaling for encoding
  • Encoding depth optimization

Usage Guidelines

  1. Feature Analysis: Understand data dimensionality and structure
  2. Encoding Selection: Choose encoding based on data type and qubit budget
  3. Scaling: Apply appropriate normalization for encoding method
  4. Depth Analysis: Balance encoding expressivity with circuit depth
  5. Verification: Validate encoded states capture relevant features

Tools/Libraries

  • PennyLane
  • Qiskit Machine Learning
  • Cirq
  • TensorFlow Quantum
  • NumPy