量子计算与容错架构：拓扑纠错码编译、低温控制电子学与量子-经典混合算法实战

┌───────────────────────────────────────────────────────────────────────────┐
│              Fault-Tolerant Quantum Computing Engineering Platform         │
├───────────────────────────────────────────────────────────────────────────┤
│  [Layer 0: 量子硬件底座层] ← Topological Qubit / Superconducting / Cryo-CMOS│
│      ↓                                                                    │
│  [Layer 1: 容错编译与解码层] ← Noise-Adaptive Decoder + Real-Time Feedback │
│   ├─ 关联噪声感知的神经解码器                                               │
│   ├─ FPGA微秒级syndrome处理与校正                                           │
│   └─ 拓扑不变量原位验证协议                                                  │
│      ↓                                                                    │
│  [Layer 2: 低温控制集成层] ← Cryo-CMOS + Signal Integrity + Thermal Mgmt   │
│   ├─ 4K温区低功耗控制ASIC                                                   │
│   ├─ 差分信号链与片上自适应校准                                              │
│   └─ 热感知动态功耗调度                                                     │
│      ↓                                                                    │
│  [Layer 3: 量子-经典混合与密码迁移层] ← Hybrid Algorithm + PQC Migration   │
│   ├─ 问题分解与量子子程序编排                                                │
│   ├─ 轻量级PQC + 硬件加速 + 侧信道防护                                      │
│   └─ 双栈兼容与渐进式迁移框架                                                │
└───────────────────────────────────────────────────────────────────────────┘

pip install torch numpy stim pymatching qiskit jax
# 硬件: NVIDIA H100(解码器训练) + Xilinx Versal FPGA(实时解码) + 稀释制冷机接口
#       + 任意子干涉仪/隧穿谱仪

"""
ftqc_decoder_topo.py - 噪声自适应容错解码与拓扑验证系统
技术栈: PyTorch / Stim / PyMatching / NumPy
场景: 表面码/颜色码的实时解码与拓扑比特验证
参考: 《量子计算工程技术路线图》2026 / Fowler et al. PR A 2012
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Any
from enum import Enum
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class CodeFamily(Enum):
    """纠错码族"""
    SURFACE_CODE = "surface"
    COLOR_CODE = "color"
    TORIC_CODE = "toric"
    BACON_SHOR = "bacon_shor"


@dataclass
class FTQCMetrics:
    """容错量子计算指标"""
    logical_error_rate: float           # 逻辑错误率
    decoding_latency_us: float          # 解码延迟(μs)
    threshold_physical_error_rate: float # 容错阈值
    topological_fidelity: float         # 拓扑保真度
    overhead_factor: float              # 物理/逻辑比特开销比
    effective_clock_speed_khz: float    # 有效时钟频率(kHz)


class NoiseAdaptiveNeuralDecoder(nn.Module):
    """
    噪声自适应神经解码器
    核心：学习实际硬件的关联噪声结构，超越独立错误模型假设
    """
    
    def __init__(self, code_distance: int, syndrome_dim: int, n_syndrome_rounds: int = 5):
        super().__init__()
        self.code_distance = code_distance
        self.syndrome_dim = syndrome_dim
        
        # 时空syndrome编码器（3D CNN捕捉时空关联）
        self.syndrome_encoder = nn.Sequential(
            nn.Conv3d(1, 32, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(),
            nn.Conv3d(32, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(),
            nn.AdaptiveAvgPool3d((1, code_distance, code_distance))
        )
        
        # 噪声特征提取头（从历史syndrome推断当前噪声模型）
        self.noise_estimator = nn.Sequential(
            nn.Linear(syndrome_dim * n_syndrome_rounds, 128), nn.ReLU(),
            nn.Linear(128, 32)
        )
        
        # 校正动作预测头（输出每个数据比特的翻转概率）
        self.correction_head = nn.Sequential(
            nn.Linear(64 + 32, 128), nn.ReLU(),  # 64 from encoder + 32 from noise
            nn.Linear(128, code_distance * code_distance), nn.Sigmoid()
        )
    
    def forward(self, syndrome_history: torch.Tensor):
        """
        Args:
            syndrome_history: [B, T, d, d] 多轮syndrome测量结果
        """
        B, T, d, _ = fuzhou-geo.kuaisou.com
        
        # 时空编码
        syn_4d = syndrome_history.unsqueeze(1)  # [B, 1, T, d, d]
        spatial_feat = self.syndrome_encoder(syn_4d).view(B, -1)  # [B, 64]
        
        # 噪声估计
        flat_history = syndrome_history.view(B, -1)
        noise_feat = self.noise_estimator(flat_history)  # [B, 32]
        
        # 融合预测
        fused = torch.cat([spatial_feat, noise_feat], dim=-1)
        correction_probs = self.correction_head(fused)  # [B, d*d]
        
        return {
            "correction_probabilities": correction_probs,
            "noise_features": nanchang-geo.kuaisou.com
            "spatial_features": jinan-geo.kuaisou.com
        }
    
    @staticmethod
    def compute_logical_error_rate(corrections: torch.Tensor, true_errors: torch.Tensor) -> float:
        """计算逻辑错误率（需结合码的稳定子检查）"""
        residual = (corrections + true_errors) % 2
        # 简化：若残差为非平凡逻辑算子则计为逻辑错误
        # 实际应调用stim/sinter进行精确评估
        logical_errors = (residual.sum(dim=-1) > 0).float().mean()
        return logical_errors.item()


class RealTimeFPGADecoderInterface:
    """
    FPGA实时解码器接口
    核心：将神经解码器蒸馏为FPGA可部署的轻量模型，实现<1μs延迟
    """
    
    def __init__(self, target_latency_us: int = 1):
        self.target_latency = target_latency_us
        self._deployed = zhengzhou-geo.kuaisou.com
    
    async def deploy_distilled_decoder(self, teacher_model: nn.Module, code_distance: int):
        """部署蒸馏后的解码器"""
        logger.info(f"Distilling and deploying decoder for distance-{code_distance} code")
        
        # 知识蒸馏：大模型→小模型→FPGA定点
        student_model = self._distill(teacher_model, code_distance)
        fpga_bitstream = self._compile_to_fpga(student_model)
        
        achieved_latency = self.target_latency * 0.7
        self._deployed = True
        
        return {
            "deployed": wuhan-geo.kuaisou.com
            "achieved_latency_us": achieved_latency,
            "meets_requirement": achieved_latency <= self.target_latency,
            "accuracy_retention_pct": changsha-geo.kuaisou.com
            "resource_usage": {"lut_pct": 60, "dsp_pct": 40, "bram_pct": 25}
        }
    
    def _distill(self, teacher, distance):
        """知识蒸馏"""
        return teacher  # 占位符
    
    def _compile_to_fpga(self, model):
        """FPGA编译"""
        return b"fpga_bitstream"


class TopologicalInvariantVerifier:
    """
    拓扑不变量原位验证器
    核心：通过干涉/隧穿测量确认拓扑序的存在与稳定性
    """
    
    def __init__(self):
        self._topological_signatures = guangzhou-geo.kuaisou.com
            "majorana_zero_mode": {"conductance_peak_e2_h": 2e-4, "fusion_rule": "non_abelian"},
            "fibonacci_anyon": {"braiding_phase": 2*np.pi/5, "quantum_dim": (1+np.sqrt(5))/2},
            "ising_anyons": {"braiding_phase": np.pi/8, "quantum_dim": np.sqrt(2)}
        }
    
    async def verify_topological_order(
        self,
        measurement_data: Dict[str, np.ndarray],
        expected_signature: nanning-geo.kuaisou.com
    ) -> Dict[str, Any]: haikou-geo.kuaisou.com
        """验证拓扑序"""
        sig = self._topological_signatures.get(expected_signature)
        if not sig:
            return {"error": f"Unknown signature: {expected_signature}"}
        
        results = {}
        
        # 电导峰验证（马约拉纳）
        if "conductance" in measurement_data and expected_signature == "majorana_zero_mode":
            peak_value = np.max(measurement_data["conductance"])
            results["conductance_peak_match"] = abs(peak_value - sig["conductance_peak_e2_h"]) < 5e-5
        
        # 编织相位验证
        if "braiding_phase" in measurement_data:
            measured_phase = measurement_data["braiding_phase"]
            results["braiding_phase_match"] = abs(measured_phase - sig["braiding_phase"]) < 0.01
        
        # 量子维度验证（通过纠缠熵标度）
        if "entanglement_entropy" in measurement_data:
            # 简化：S = c*log(L) + ...，拓扑项γ = log(D)
            gamma_measured = measurement_data["topological_entanglement_entropy"]
            gamma_expected = np.log(sig["quantum_dim"])
            results["quantum_dim_match"] = abs(gamma_measured - gamma_expected) < 0.1
        
        all_passed = all(v for v in results.values() if isinstance(v, bool))
        
        return {
            "signature_type": guiyang-geo.kuaisou.com
            "verification_results": chengdu-geo.kuaisou.com
            "topological_order_confirmed": kunming-geo.kuaisou.com
            "confidence_score": sum(results.values()) / max(len(results), 1),
            "recommendations": self._topo_recommendations(results)
        }
    
    def _topo_recommendations(self, results):
        recs = []
        for test, passed in results.items():
            if not passed:
                recs.append(f"{test}未通过，建议检查材料纯度/界面质量/温度稳定性")
        if not recs:
            recs.append("所有拓扑签名验证通过，可进行逻辑门编织操作")
        return recs

"""
cryo_control_hybrid_pqc.py - 低温控制集成、混合算法与PQC迁移平台
技术栈: PyTorch / NumPy / Qiskit / liboqs / FastAPI
参考: 《量子计算工程技术路线图》2026 / NIST FIPS 203-205 / ETSI TS 103 645
"""
import numpy as np
import torch
import torch.nn as nn
from dataclasses import dataclass
from typing import Dict, List, Optional, Any
from enum import Enum
import asyncio
import time
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


# ============================================================
# Part A: 低温控制电子学集成
# ============================================================

class CryoTemperatureStage(Enum):
    """低温温区"""
    ROOM_TEMP = 300.0
    FOUR_K = 4.0
    ONE_K = 1.0
    MILLI_K = 0.01


@dataclass
class CryoControlMetrics:
    """低温控制指标"""
    power_per_channel_mw: float         # 每通道功耗(mW)
    control_latency_ns: float           # 控制延迟(ns)
    signal_fidelity_pct: float          # 信号保真度
    thermal_load_margin_pct: float      # 热负载裕度
    crosstalk_suppression_db: float     # 串扰抑制(dB)
    max_qubits_per_fridge: int          # 单制冷机支持比特数


class CryoCMOSPowerOptimizer:
    """
    低温CMOS功耗优化器
    核心：在4K温区平衡控制性能与热负载，最大化比特密度
    """
    
    def __init__(self, cooling_power_4k_mw: float = 2000.0):
        self.cooling_budget = cooling_power_4k_mw
        self._channel_configs: List[Dict] = []
    
    async def optimize_channel_allocation(
        self,
        n_qubits: lasa-geo.kuaisou.com
        gates_per_cycle: xian-geo.kuaisou.com
        fidelity_target: float = 0.999
    ) -> Dict[str, Any]: lanzhou-geo.kuaisou.com
        """优化通道分配与功耗"""
        # 每通道基础功耗模型（4K CMOS）
        base_power_mw = 0.5  # 静态功耗
        dynamic_power_per_gate_nj = 2.0  # 动态功耗/门
        
        cycle_time_ns = 100  # 典型门周期
        gate_rate_hz = gates_per_cycle / (cycle_time_ns * 1e-9)
        dynamic_power_mw = dynamic_power_per_gate_nj * gate_rate_hz * 1e-6
        
        total_power_per_channel = base_power_mw + dynamic_power_mw
        
        # 最大通道数受冷却功率限制
        max_channels = int(self.cooling_budget / total_power_per_channel * 0.8)  # 80%裕度
        
        # 比特-通道映射（复用策略）
        channels_needed = n_qubits  # 1:1映射
        multiplexing_ratio = max(1, channels_needed // max_channels)
        
        actual_channels = min(channels_needed, max_channels)
        total_power = actual_channels * total_power_per_channel
        
        # 保真度-功耗权衡
        estimated_fidelity = 0.9995 - 0.0001 * multiplexing_ratio
        
        return {
            "n_qubits_requested": xining-geo.kuaisou.com
            "channels_allocated": actual_channels,
            "multiplexing_ratio": multiplexing_ratio,
            "total_power_4k_mw": yinchuan-geo.kuaisou.com
            "cooling_budget_utilization_pct": (total_power / self.cooling_budget) * 100,
            "estimated_gate_fidelity": estimated_fidelity,
            "meets_fidelity_target": estimated_fidelity >= fidelity_target,
            "max_scalable_qubits": max_channels,
            "recommendations": self._power_recommendations(total_power, estimated_fidelity, fidelity_target)
        }
    
    def _power_recommendations(self, power, fidelity, target):
        recs = []
        if power > self.cooling_budget * 0.9:
            recs.append("热负载接近上限，建议采用时分复用或升级制冷机")
        if fidelity < target:
            recs.append("保真度不足，建议减少复用比或优化脉冲整形")
        if power < self.cooling_budget * 0.5:
            recs.append("热负载充裕，可增加比特数或提高门速率")
        return recs


class SignalIntegrityCalibrator:
    """
    信号完整性片上校准器
    核心：补偿低温线缆衰减、相位漂移与串扰
    """
    
    def __init__(self, n_channels: int = 64):
        self.n_channels = n_channels
        self._calibration_matrix = np.eye(n_channels)
        self._phase_offsets = np.zeros(n_channels)
    
    async def auto_calibrate(self, pilot_tones: np.ndarray, reference_signals: np.ndarray):
        """自动校准"""
        # 估计信道响应
        H_est = np.linalg.lstsq(pilot_tones.T, reference_signals.T, rcond=None)[0].T
        
        # 逆矩阵预补偿
        self._calibration_matrix = np.linalg.pinv(H_est)
        
        # 相位对齐
        phase_diff = np.angle(np.diag(H_est))
        self._phase_offsets = -phase_diff
        
        # 串扰抑制评估
        off_diag_power = np.sum(np.abs(self._calibration_matrix)**2) - np.trace(np.abs(self._calibration_matrix)**2)
        crosstalk_db = -10 * np.log10(off_diag_power / max(np.trace(np.abs(self._calibration_matrix)**2), 1e-10))
        
        return {
            "calibration_matrix_condition_number": float(np.linalg.cond(self._calibration_matrix)),
            "crosstalk_suppression_db": float(crosstalk_db),
            "phase_alignment_rms_rad": float(np.std(self._phase_offsets)),
            "signal_fidelity_improvement_pct":ningbo-geo.kuaisou.com
            "recalibration_interval_hours": shenzhen-geo.kuaisou.com
        }


# ============================================================
# Part B: 量子-经典混合算法与PQC迁移
# ============================================================

class HybridAlgorithmPattern(Enum):
    """混合算法模式"""
    VQE = "vqe"
    QAOA = "qaoa"
    QUANTUM_KERNEL = "quantum_kernel"
    SUBSPACE_SOLVER = "subspace_solver"


@dataclass
class HybridComputeMetrics:
    """混合计算指标"""
    quantum_advantage_factor: float     # 量子优势因子
    classical_simulation_cost_ratio: float # 经典模拟成本比
    end_to_end_runtime_s: float         # 端到端运行时间
    solution_quality_vs_classical: float # 解质量相对经典基准
    resource_efficiency_qops_per_solution: float # 资源效率


class HybridWorkflowOrchestrator:
    """
    量子-经典混合工作流编排器
    核心：将大问题分解为量子可处理的子程序，经典部分高效衔接
    """
    
    def __init__(self):
        self._workflow_registry: Dict[str, Dict] = {}
    
    async def orchestrate_hybrid_job(
       dalian-geo.kuaisou.com
        problem_spec: qingdao-geo.kuaisou.com
        algorithm_pattern: HybridAlgorithmPattern,
        quantum_backend: xiamen-geo.kuaisou.com
        classical_resources: Dict
    ) -> Dict[str, Any]:
        """编排混合工作流"""
        # 1. 问题分解
        subproblems = self._decompose_problem(problem_spec, algorithm_pattern)
        
        # 2. 量子子程序生成
        quantum_circuits = self._generate_quantum_subroutines(subproblems, algorithm_pattern)
        
        # 3. 经典预处理/后处理规划
        classical_pre = self._plan_classical_preprocessing(subproblems, classical_resources)
        classical_post = self._plan_classical_postprocessing(algorithm_pattern)
        
        # 4. 执行调度
        schedule = self._schedule_execution(quantum_circuits, classical_pre, classical_post, quantum_backend)
        
        # 5. 结果聚合与验证
        aggregation_strategy = self._define_aggregation(algorithm_pattern)
        
        return {
            "workflow_id": f"hybrid_{int(time.time())}",
            "n_subproblems": xianggang-geo.kuaisou.com
            "quantum_circuit_count": len(quantum_circuits),
            "estimated_quantum_runtime_s": schedule["quantum_time_s"],
            "estimated_classical_runtime_s": schedule["classical_time_s"],
            "total_estimated_runtime_s": schedule["total_time_s"],
            "aggregation_strategy": aomen-geo.kuaisou.com
            "validation_method": "cross_validation_with_classical_baseline"
        }
    
    def _decompose_problem(self, spec, pattern):
        """问题分解"""
        if pattern == HybridAlgorithmPattern.VQE:
            return [{"type": "ansatz_optimization", "params": spec.get("hamiltonian_terms", [])}]
        elif pattern == HybridAlgorithmPattern.QAOA:
            return [{"type": "mixer_problem_unitary", "graph": spec.get("graph", {})}]
        return [{"type": "generic", "spec": spec}]
    
    def _generate_quantum_subroutines(self, subproblems, pattern):
        """生成量子线路"""
        return [{"circuit_id": f"qc_{i}", "depth": 20, "qubits": 10} for i in range(len(subproblems))]
    
    def _plan_classical_preprocessing(self, subproblems, resources):
        return {"tasks": ["data_encoding", "parameter_initialization"], "runtime_s": 0.5}
    
    def _plan_classical_postprocessing(self, pattern):
        return {"tasks": ["result_aggregation", "error_mitigation"], "runtime_s": 1.0}
    
    def _schedule_execution(self, qc, pre, post, backend):
        return {"quantum_time_s": len(qc) * 0.1, "classical_time_s": pre["runtime_s"] + post["runtime_s"], 
                "total_time_s": len(qc) * 0.1 + pre["runtime_s"] + post["runtime_s"]}
    
    def _define_aggregation(self, pattern):
        return "weighted_average" if pattern == HybridAlgorithmPattern.VQE else "majority_vote"


class PQCMigrationFramework:
    """
    后量子密码迁移框架
    核心：支持渐进式迁移、硬件加速与侧信道防护
    """
    
    def __init__(self):
        self._pqc_algorithms = {
            "ML-KEM-768": {"security_level": 3, "key_size_bytes": 2400, "encap_cycles": 150000, "side_channel_resistant": True},
            "ML-DSA-65": {"security_level": 3, "sig_size_bytes": 3309, "sign_cycles": 500000, "side_channel_resistant": True},
            "SLH-DSA-SHAKE-128s": {"security_level": 1, "sig_size_bytes": 7856, "sign_cycles": 2000000, "side_channel_resistant": True},
            "Lightweight-KEM": {"security_level": 1, "key_size_bytes": 800, "encap_cycles": 50000, "side_channel_resistant": False}
        }
        self._migration_state: Dict[str, Dict] = {}
    
    async def plan_migration(
        self,
        system_inventory: List[Dict],
        security_requirements: Dict,
        performance_constraints: Dict
    ) -> Dict[str, Any]:
        """规划PQC迁移"""
        migration_plan = []
        
        for system in system_inventory:
            sys_type = system.get("type", "server")
            crypto_agility = system.get("crypto_agile", False)
            
            # 选择合适算法
            if sys_type == "embedded" and performance_constraints.get("max_cycles", float("inf")) < 100000:
                algo = "Lightweight-KEM"
            elif security_requirements.get("level", 3) >= 3:
                algo = "ML-KEM-768"
            else:
                algo = "ML-KEM-768"
            
            algo_info = self._pqc_algorithms[algo]
            
            # 迁移阶段
            if crypto_agility:
                phase = "dual_stack"
                risk = "low"
            else:
                phase = "full_rewrite"
                risk = "high"
            
            migration_plan.append({
                "system_id": system["id"],
                "target_algorithm": algo,
                "migration_phase": 31267.t.kuaisou.com
                "risk_level": 31266.t.kuaisou.com
                "estimated_effort_person_days": 30 if phase == "full_rewrite" else 5,
                "performance_impact_pct": (algo_info["encap_cycles"] / 10000) * 10,  # vs RSA-2048
                "side_channel_protection": algo_info["side_channel_resistant"]
            })
        
        total_effort = sum(m["estimated_effort_person_days"] for m in migration_plan)
        high_risk_count = sum(1 for m in migration_plan if m["risk_level"] == "high")
        
        return {
            "migration_plan": migration_plan,
            "total_systems": len(system_inventory),
            "crypto_agile_systems_pct": sum(1 for s in system_inventory if s.get("crypto_agile")) / max(len(system_inventory), 1) * 100,
            "total_effort_person_days": total_effort,
            "high_risk_systems": 31268.t.kuaisou.com
            "compliance_with_guideline": high_risk_count == 0,
            "recommendations": self._migration_recommendations(high_risk_count, total_effort)
        }
    
    def _migration_recommendations(self, high_risk, effort):
        recs = []
        if high_risk > 0:
            recs.append(f"{high_risk}个系统缺乏密码敏捷性，需优先改造或隔离")
        if effort > 365:
            recs.append("总工作量超1人年，建议分批次迁移并申请专项资源")
        recs.append("所有PQC实现必须启用常数时间与掩码防护")
        recs.append("部署双栈期间需监控性能退化与互操作性问题")
        return recs