量子机器学习分类器需求

【免费下载链接】context-engineering-intro Context engineering is the new vibe coding - it's the way to actually make AI coding assistants work. Claude Code is the best for this so that's what this repo is centered around, but you can apply this strategy with any AI coding assistant! 项目地址: https://gitcode.com/gh_mirrors/co/context-engineering-intro

功能要求

• 实现量子支持向量机（QSVM）分类器
• 集成经典-量子混合数据处理管道
• 提供量子特征映射和核函数
• 支持多种量子后端（模拟器和真实量子计算机）

技术要求

• 使用Qiskit或PennyLane量子计算框架
• 集成scikit-learn兼容接口
• 实现量子梯度下降优化
• 提供完整的测试覆盖

### 4. 生成产品需求提示（PRP）
在Claude Code中运行：
```bash
/generate-pydantic-ai-prp PRPs/INITIAL.md

5. 执行PRP构建量子分类器

/execute-pydantic-ai-prp PRPs/generated_prp.md

📁 项目结构与最佳实践

遵循context-engineering-intro的模板结构，您的量子机器学习项目将自动获得专业组织：

quantum-classifier-project/
├── CLAUDE.md                    # 项目全局规则和量子计算特定约定
├── agents/                      # AI代理实现
│   └── quantum_ml_agent/       # 量子机器学习专用代理
│       ├── quantum_tools.py    # 量子计算工具函数
│       ├── qsvm_classifier.py  # 量子支持向量机实现
│       └── hybrid_pipeline.py  # 经典-量子混合管道
├── examples/                    # 量子机器学习模式参考
│   ├── quantum_data_preprocessing.py
│   ├── quantum_feature_maps.py
│   └── quantum_kernel_methods.py
└── tests/                       # 量子算法测试套件
    ├── test_quantum_classifier.py
    └── test_hybrid_pipeline.py

【免费下载链接】context-engineering-intro Context engineering is the new vibe coding - it's the way to actually make AI coding assistants work. Claude Code is the best for this so that's what this repo is centered around, but you can apply this strategy with any AI coding assistant! 项目地址: https://gitcode.com/gh_mirrors/co/context-engineering-intro