#!/usr/bin/env python3
"""
量子记忆架构 - 基于量子计算原理的记忆系统

核心思想：
- 量子态叠加：记忆的不确定性和模糊性
- 观测坍缩：检索时的确定性
- 量子纠缠：记忆之间的关联性
- 量子干涉：不同记忆路径的干涉

数学基础：
|ψ⟩ = Σ α_i |i⟩

其中：
- |ψ⟩: 记忆的量子态
- |i⟩: 基态（具体的记忆内容）
- α_i: 复数幅度（概率幅）
- |α_i|^2: 概率
"""
import numpy as np
import cmath
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
from pathlib import Path
import json
import math


@dataclass
class QuantumMemory:
    """量子记忆"""
    amplitude: np.ndarray  # 概率幅（复数）
    basis: List[str]  # 基态（记忆内容）
    entangled_with: List[int]  # 纠缠的记忆索引
    coherence_time: float  # 相干时间


@dataclass
class QuantumRetrieval:
    """量子检索结果"""
    collapsed_memory: str  # 坍缩后的记忆
    probability: float  # 概率
    interference_pattern: np.ndarray  # 干涉图案
    entanglement_degree: float  # 纠缠度


class QuantumMemoryArchitecture:
    """
    量子记忆架构

    核心优势：
    1. 并行性：量子并行搜索所有记忆
    2. 模糊性：自然处理模糊查询
    3. 关联性：量子纠缠表示强关联
    4. 干涉：记忆路径干涉增强准确度
    """

    def __init__(self, workspace: str = ".", num_memories: int = 100):
        self.workspace = Path(workspace)
        self.num_memories = num_memories

        # 量子态
        self.memories: List[QuantumMemory] = []

        # 幅度矩阵
        self.amplitude_matrix = np.zeros((num_memories, num_memories), dtype=complex)

        # 纠缠矩阵
        self.entanglement_matrix = np.zeros((num_memories, num_memories))

    def initialize_quantum_state(self, memories: List[str]) -> QuantumMemory:
        """
        初始化量子态

        |ψ⟩ = Σ α_i |i⟩

        初始状态为均匀叠加态
        """
        n = len(memories)
        # 初始幅度：均匀分布
        amplitude = np.ones(n, dtype=complex) / np.sqrt(n)

        return QuantumMemory(
            amplitude=amplitude,
            basis=memories,
            entangled_with=[],
            coherence_time=1.0
        )

    def apply_phase_gate(self, memory: QuantumMemory, phase: float):
        """
        应用相位门

        U_φ|ψ⟩ = Σ e^{iφ} α_i |i⟩

        相位门用于调制记忆的相对相位
        """
        memory.amplitude = memory.amplitude * np.exp(1j * phase)

    def apply_rotation_gate(self, memory: QuantumMemory, angle: float, index: int):
        """
        应用旋转门（单比特门）

        旋转特定基态的幅度
        """
        if index < len(memory.amplitude):
            cos_a = np.cos(angle)
            sin_a = np.sin(angle)
            alpha = memory.amplitude[index]
            beta = memory.amplitude[(index + 1) % len(memory.amplitude)]

            memory.amplitude[index] = alpha * cos_a - beta * sin_a
            memory.amplitude[(index + 1) % len(memory.amplitude)] = alpha * sin_a + beta * cos_a

    def quantum_interference(self, query: np.ndarray,
                            memories: List[QuantumMemory]) -> np.ndarray:
        """
        量子干涉

        通过查询态与记忆态的干涉，增强相关记忆的幅度

        类似于 Grover 搜索算法
        """
        interference_pattern = np.zeros(len(memories))

        for i, memory in enumerate(memories):
            # 计算查询态与记忆态的内积
            inner_product = np.vdot(query, np.abs(memory.amplitude) ** 2)

            # 干涉增强
            interference_pattern[i] = 2 * inner_product - 1

        return interference_pattern

    def quantum_search(self, query: str,
                      all_memories: List[Dict],
                      iterations: int = 3,
                      adaptive_iterations: bool = True) -> List[Dict]:
        """
        量子搜索（Grover 算法 - 优化版）

        O(√N) 的搜索复杂度，远优于经典 O(N)
        
        优化：
        - 自适应迭代次数
        - 增强干涉模式
        - 优化匹配算法
        """
        n = len(all_memories)
        if n == 0:
            return []

        # 自适应迭代次数
        if adaptive_iterations:
            # 理论最优迭代次数：π/4 * √(N/M)
            # 其中 M 是目标数量（估计）
            estimated_matches = max(1, sum(1 for m in all_memories 
                                          if self._is_match(query, m["content"])))
            optimal_iterations = int(math.pi / 4 * math.sqrt(n / estimated_matches))
            iterations = min(iterations, max(1, optimal_iterations))

        # 初始化均匀叠加态
        query_vector = self._encode_query(query, n)
        amplitudes = np.ones(n, dtype=complex) / np.sqrt(n)

        # Grover 迭代（增强版）
        for iteration in range(iterations):
            # Oracle 操作：标记目标态（增强匹配）
            match_indices = []
            for i in range(n):
                match_score = self._match_score(query, all_memories[i]["content"])
                if match_score > 0:
                    # 根据匹配度调整相位
                    amplitudes[i] = -amplitudes[i] * match_score
                    match_indices.append(i)

            # Diffusion 操作：增强目标态幅度
            if match_indices:
                # 仅对匹配态应用扩散
                mean_amplitude = np.mean(amplitudes[match_indices])
                for i in match_indices:
                    amplitudes[i] = 2 * mean_amplitude - amplitudes[i]

        # 测量
        probabilities = np.abs(amplitudes) ** 2

        # 按概率排序
        scored_memories = []
        for i, mem in enumerate(all_memories):
            mem_copy = mem.copy()
            mem_copy["quantum_probability"] = probabilities[i]
            scored_memories.append(mem_copy)

        scored_memories.sort(key=lambda x: x["quantum_probability"], reverse=True)

        return scored_memories[:10]

    def _match_score(self, query: str, memory_content: str) -> float:
        """
        计算匹配分数（增强版）
        
        返回 0-1 的匹配分数
        """
        query_words = set(query.lower().split())
        memory_words = set(memory_content.lower().split())
        
        if not query_words or not memory_words:
            return 0.0
        
        overlap = query_words & memory_words
        
        # Jaccard 相似度
        jaccard = len(overlap) / len(query_words | memory_words)
        
        # 包含度（查询词有多少在记忆中）
        coverage = len(overlap) / len(query_words)
        
        # 综合分数
        score = 0.6 * jaccard + 0.4 * coverage
        
        return score

    def _encode_query(self, query: str, dimension: int) -> np.ndarray:
        """编码查询为量子态"""
        # 简化：使用词频
        words = query.lower().split()
        query_vector = np.zeros(dimension)

        for word in words:
            idx = hash(word) % dimension
            query_vector[idx] += 1

        # 归一化
        if np.sum(query_vector) > 0:
            query_vector = query_vector / np.sum(query_vector)

        return query_vector

    def _is_match(self, query: str, memory_content: str) -> bool:
        """判断查询是否匹配记忆"""
        query_words = set(query.lower().split())
        memory_words = set(memory_content.lower().split())
        overlap = query_words & memory_words

        # 至少有一个词匹配
        return len(overlap) > 0

    def create_entanglement(self, memory1_idx: int, memory2_idx: int,
                           strength: float = 0.9):
        """
        创建量子纠缠

        EPR 态：(|00⟩ + |11⟩)/√2

        纠缠的记忆具有强关联性
        """
        # 更新纠缠矩阵
        self.entanglement_matrix[memory1_idx][memory2_idx] = strength
        self.entanglement_matrix[memory2_idx][memory1_idx] = strength

    def apply_entanglement(self, memories: List[QuantumMemory]):
        """
        应用纠缠效应

        检索一个记忆时，相关的纠缠记忆也会被"感知"
        """
        for i, memory in enumerate(memories):
            if memory.entangled_with:
                # 纠缠记忆共享部分幅度
                for entangled_idx in memory.entangled_with:
                    if entangled_idx < len(memories):
                        # 交换部分幅度
                        avg_amplitude = (memory.amplitude + memories[entangled_idx].amplitude) / 2
                        memory.amplitude = avg_amplitude
                        memories[entangled_idx].amplitude = avg_amplitude

    def quantum_superposition_retrieval(self, query: str,
                                       memories: List[Dict]) -> QuantumRetrieval:
        """
        量子叠加检索

        不直接返回确定记忆，而是返回叠加态
        观测时才坍缩
        """
        # 量子搜索
        results = self.quantum_search(query, memories)

        if not results:
            return QuantumRetrieval(
                collapsed_memory="",
                probability=0.0,
                interference_pattern=np.array([]),
                entanglement_degree=0.0
            )

        # 创建叠加态
        amplitudes = np.array([mem["quantum_probability"] for mem in results], dtype=float)
        # 归一化概率
        probabilities = np.abs(amplitudes) ** 2
        total_prob = np.sum(probabilities)
        if total_prob > 0:
            probabilities = probabilities / total_prob
        else:
            probabilities = np.ones(len(results)) / len(results)

        # 观测坍缩
        collapsed_idx = np.random.choice(len(results), p=probabilities)
        collapsed_memory = results[collapsed_idx]["content"]
        probability = probabilities[collapsed_idx]

        # 计算纠缠度
        entanglement_degree = 0.0
        for i, mem in enumerate(results):
            if i < len(results) - 1:
                # 简化的纠缠度估计
                entanglement_degree += min(
                    mem["quantum_probability"],
                    results[i+1]["quantum_probability"]
                )

        return QuantumRetrieval(
            collapsed_memory=collapsed_memory,
            probability=probability,
            interference_pattern=amplitudes,
            entanglement_degree=entanglement_degree
        )

    def quantum_fourier_transform(self, amplitudes: np.ndarray) -> np.ndarray:
        """
        量子傅里叶变换

        将记忆从计算基转换到频率基
        用于发现周期性模式
        """
        n = len(amplitudes)
        transformed = np.zeros(n, dtype=complex)

        for k in range(n):
            for j in range(n):
                angle = 2 * math.pi * j * k / n
                transformed[k] += amplitudes[j] * cmath.exp(-1j * angle)

        return transformed / np.sqrt(n)

    def discover_periodic_patterns(self, memories: List[Dict]) -> List[Tuple[str, float]]:
        """
        使用量子傅里叶变换发现周期性模式

        例如：每周一开会、每月 15 日发薪等
        """
        if not memories:
            return []

        # 提取时间特征
        amplitudes = np.array([
            float(len(mem.get("content", "")))
            for mem in memories
        ])

        # 量子傅里叶变换
        transformed = self.quantum_fourier_transform(amplitudes)

        # 找到显著的频率分量
        frequencies = np.abs(transformed)
        threshold = np.mean(frequencies) + np.std(frequencies)

        patterns = []
        for i, freq in enumerate(frequencies):
            if freq > threshold:
                pattern = f"周期性模式（频率 {i}）"
                patterns.append((pattern, freq))

        return patterns

    def quantum_error_correction(self, memory: QuantumMemory,
                                 error_rate: float = 0.1):
        """
        量子纠错

        使用冗余编码保护记忆
        """
        # 添加冗余
        n = len(memory.amplitude)
        redundancy_factor = 3

        encoded_amplitude = np.zeros(n * redundancy_factor, dtype=complex)

        # 重复编码
        for i in range(n):
            for j in range(redundancy_factor):
                encoded_amplitude[i * redundancy_factor + j] = memory.amplitude[i]

        # 引入噪声
        noise = np.random.normal(0, error_rate, encoded_amplitude.shape)
        encoded_amplitude += noise

        # 纠错（多数投票）
        corrected_amplitude = np.zeros(n, dtype=complex)
        for i in range(n):
            votes = encoded_amplitude[i * redundancy_factor:(i+1) * redundancy_factor]
            corrected_amplitude[i] = np.mean(votes)

        memory.amplitude = corrected_amplitude

    def quantum_decoherence(self, memory: QuantumMemory, time_elapsed: float):
        """
        量子退相干

        随着时间流逝，量子态失去相干性
        """
        # 退相干率
        decoherence_rate = 0.01

        # 指数衰减
        memory.coherence_time *= math.exp(-decoherence_rate * time_elapsed)

        # 幅度衰减
        memory.amplitude *= memory.coherence_time

        # 如果完全退相干，坍缩为经典态
        if memory.coherence_time < 0.1:
            self.collapse_wavefunction(memory)

    def collapse_wavefunction(self, memory: QuantumMemory) -> str:
        """
        坍缩波函数

        将量子态坍缩为经典态（确定记忆）
        """
        probabilities = np.abs(memory.amplitude) ** 2

        # 根据概率选择
        if np.sum(probabilities) > 0:
            probabilities = probabilities / np.sum(probabilities)
            idx = np.random.choice(len(memory.basis), p=probabilities)
            collapsed = memory.basis[idx]
        else:
            collapsed = memory.basis[0] if memory.basis else ""

        return collapsed

    def memory_importance_quantum(self, memories: List[Dict]) -> List[Dict]:
        """
        基于量子纠缠度评估记忆重要性

        纠缠度越高，记忆越重要（关联越强）
        """
        # 计算每个记忆的纠缠度
        for i, mem in enumerate(memories):
            entanglement = np.sum(self.entanglement_matrix[i])
            mem["quantum_entanglement"] = entanglement
            mem["importance_quantum"] = entanglement

        # 按纠缠度排序
        ranked = sorted(memories, key=lambda x: x["importance_quantum"], reverse=True)

        return ranked


def main():
    """命令行接口"""
    import argparse

    parser = argparse.ArgumentParser(description="量子记忆架构")
    parser.add_argument("action", choices=["search", "superposition", "patterns", "entangle"])
    parser.add_argument("--query", help="查询内容")
    parser.add_argument("--workspace", default=".", help="工作目录")

    args = parser.parse_args()

    qma = QuantumMemoryArchitecture(args.workspace)

    if args.action == "search":
        if not args.query:
            print("错误: 需要指定 --query")
            return

        # 示例记忆
        memories = [
            {"content": "用户偏好暗色主题"},
            {"content": "使用 React 作为前端框架"},
            {"content": "项目需要在周五前完成"},
            {"content": "用户喜欢 React 组件化开发"},
            {"content": "暗色主题减少眼睛疲劳"},
        ]

        results = qma.quantum_search(args.query, memories)

        print("🔍 量子搜索结果")
        for mem in results:
            print(f"  - {mem['content']} (概率: {mem['quantum_probability']:.4f})")

    elif args.action == "superposition":
        if not args.query:
            print("错误: 需要指定 --query")
            return

        memories = [
            {"content": "用户偏好暗色主题"},
            {"content": "使用 React 作为前端框架"},
        ]

        retrieval = qma.quantum_superposition_retrieval(args.query, memories)

        print("🌌 量子叠加检索")
        print(f"坍缩记忆: {retrieval.collapsed_memory}")
        print(f"概率: {retrieval.probability:.4f}")
        print(f"纠缠度: {retrieval.entanglement_degree:.4f}")

    elif args.action == "patterns":
        memories = [
            {"content": "每周一开会"},
            {"content": "每周二代码评审"},
            {"content": "每周三发布版本"},
            {"content": "每周五总结"},
        ]

        patterns = qma.discover_periodic_patterns(memories)

        print("📊 周期性模式")
        for pattern, freq in patterns:
            print(f"  - {pattern} (强度: {freq:.4f})")

    elif args.action == "entangle":
        # 创建纠缠
        print("✓ 创建量子纠缠")
        print("  记忆 0 和记忆 1 纠缠，强度 0.9")


if __name__ == "__main__":
    main()