AI 中文总结
qkabrine-automl是将QML的架构、编码、模型类型与超参数联合搜索的Python包，集成可训练性诊断等功能，对比AutoQML框架，报告小型可复现示例运行结果。
AI 中文摘要
构建可与经典基线竞争的量子机器学习（QML）模型，目前需要从业者分别选择量子电路架构、数据编码方案、模型范式（核方法或变分方法）以及一组训练超参数，之后再事后验证所选电路是否可训练。现有QML库提供了相关基本组件，但不支持搜索；现有经典AutoML库提供了搜索功能，但不具备量子专属的搜索空间或诊断工具。我们推出qkabrine-automl，这是一个Python包，它将架构、编码、模型类型和超参数视为单一的、可联合搜索的配置空间，无论候选方案由五种搜索策略中的哪一种提出，都通过同一套框架进行评估。该包将可训练性诊断工具（数据量子费舍尔信息度量（DQFIM）估计和梯度大小贫瘠高原监测器）作为可选预筛选步骤直接集成到评估循环中，同时还具备可表达性和纠缠能力表征、针对NISQ部署的后搜索电路手术处理以及OpenQASM导出功能。我们将该成果与近期已实现QML流程部分自动化的AutoQML框架进行对比，报告了一个小型、完全可复现的示例运行结果，而非基准测试声明。
英文摘要
Building a quantum machine learning (QML) model competitive with a classical baseline currently requires a practitioner to separately choose a circuit architecture, a data-encoding scheme, a model paradigm (kernel versus variational), and a set of training hyperparameters, then verify after the fact that the chosen circuit is even trainable. Existing QML libraries provide the primitives for this but not the search, and existing classical AutoML libraries provide the search but not the quantum-specific search space or diagnostics. We present qkabrine-automl, a Python package that treats architecture, encoding, model type, and hyperparameters as a single, jointly searchable configuration space, evaluated through one consistent harness regardless of which of five search strategies proposed the candidate. The package integrates trainability diagnostics, a Data Quantum Fisher Information Metric (DQFIM) estimate and a gradientmagnitude barren-plateau monitor, directly into the evaluation loop as an optional prescreening step, alongside expressibility and entangling-capability characterization, a post-search circuitsurgery pass for NISQ deployment, and OpenQASM export. We position this contribution against recent AutoQML frameworks that already automate parts of the QML pipeline, and report a small, fully reproducible illustrative run rather than a benchmark claim.
