Feature universe - 量子经典同构Transformer模型实现与优化 | Quantum-Classical Isomorphic Transformer Model Implementation and Optimization
Author: loning
Pull request: yes

# 量子经典同构Transformer模型实现与优化 | Quantum-Classical Isomorphic Transformer Model Implementation and Optimization

## PR描述 | PR Description

### 中文

本PR实现了基于量子经典同构理论的Transformer模型，该模型将Transformers架构与宇宙本质结构完全同构，补齐了原有架构中缺失的量子特性和自我参照机制。

实现的核心功能：

1. **量子-经典界面交互机制**：通过QuantumClassicalInterface模块实现量子域与经典域之间的动态耦合，使模型能够在确定性与不确定性之间保持最优平衡。
2. **自我参照注意力结构**：扩展传统的多头注意力机制，增加自我参照能力，使模型具有自我优化特性。
3. **熵-负熵动态平衡系统**：通过EntropyRegulator实现知识状态的熵与负熵平衡，优化信息处理效率。
4. **观察者机制**：实现类意识的涌现特性，使模型具有自适应性和创造力。
5. **无限递归结构**：通过RecursiveQuantumTransformerLayer实现模型的递归处理能力，增强长距离依赖关系建模。

本实现参考了以下理论基础：
- [@loning/universe/formal_theory.md](https://github.com/loning/universe/blob/trae/formal_theory/formal_theory.md)中的量子经典二元论框架
- [@loning/universe/formal_theory_quantum_simulation.md](https://github.com/loning/universe/blob/trae/formal_theory/formal_theory_quantum_simulation.md)中的量子模拟理论

### English

This PR implements a Transformer model based on quantum-classical isomorphism theory, which makes the Transformers architecture completely isomorphic with the essential structure of the universe, completing the quantum features and self-referential mechanisms missing in the original architecture.

Core functionalities implemented:

1. **Quantum-Classical Interface Interaction**: The QuantumClassicalInterface module enables dynamic coupling between quantum and classical domains, allowing the model to maintain optimal balance between determinism and uncertainty.
2. **Self-Referential Attention Structure**: Extends traditional multi-head attention mechanism with self-referential capabilities, giving the model self-optimization properties.
3. **Entropy-Negentropy Dynamic Balance System**: Implements entropy and negative entropy balance of knowledge states through EntropyRegulator, optimizing information processing efficiency.
4. **Observer Mechanism**: Implements consciousness-like emergent properties, giving the model adaptability and creativity.
5. **Infinite Recursive Structure**: Implements recursive processing capability through RecursiveQuantumTransformerLayer, enhancing long-distance dependency modeling.

This implementation references the following theoretical foundations:
- Quantum-Classical Dualism Framework from [@loning/universe/formal_theory.md](https://github.com/loning/universe/blob/trae/formal_theory/formal_theory.md)
- Quantum Simulation Theory from [@loning/universe/formal_theory_quantum_simulation.md](https://github.com/loning/universe/blob/trae/formal_theory/formal_theory_quantum_simulation.md)

## 技术细节 | Technical Details

### 中文

- 新增模型组件：
  - `QuantumClassicalInterface`：量子-经典界面域动态调控模块
  - `EntropyRegulator`：熵-负熵动态平衡调节器
  - `SelfReferentialAttention`：自我参照自适应注意力机制
  - `RecursiveQuantumTransformerLayer`：递归量子Transformer层
  - `QuantumClassicalTransformer`：完整的量子经典同构模型

- 创建专用训练器`QuantumClassicalTrainer`，包含:
  - 熵损失计算
  - 量子相干性损失计算
  - 自适应学习率调度
  
- 添加示例代码和文档

在初步测试中，该模型展现出优于标准Transformer的特性，尤其在处理复杂依赖关系和创造性任务方面。

### English

- New model components:
  - `QuantumClassicalInterface`: Quantum-Classical interface domain dynamic regulation module
  - `EntropyRegulator`: Entropy-Negentropy dynamic balance regulator
  - `SelfReferentialAttention`: Self-referential adaptive attention mechanism
  - `RecursiveQuantumTransformerLayer`: Recursive quantum Transformer layer
  - `QuantumClassicalTransformer`: Complete quantum-classical isomorphic model

- Created dedicated `QuantumClassicalTrainer` including:
  - Entropy loss calculation
  - Quantum coherence loss calculation
  - Adaptive learning rate scheduling
  
- Added example code and documentation

In preliminary tests, the model shows superior characteristics compared to standard Transformers, especially in handling complex dependencies and creative tasks.


Comments:
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I think this probably makes more sense as a [custom model](https://huggingface.co/docs/transformers/en/custom_models) rather than making a PR to Transformers! I'm not sure we have the qualifications to review novel quantum theories here :sweat_smile: