import random

class QuantumAssociativeMemory:
    def __init__(self, num_qubits):
        self.num_qubits = num_qubits
        self.memory = {}

    def store_pattern(self, label, pattern):
        self.memory[pattern] = label
        return {"utilization": f"{len(self.memory)}/{2**self.num_qubits}"}

    def retrieve(self, query, noise_level=0.0):
        # Mock retrieval logic
        best_match = None
        min_hamming = 1000
        corrupted = "".join(
            str(1 - int(c)) if random.random() < noise_level else c for c in query
        )
        for p, l in self.memory.items():
            hamming = sum(1 for a, b in zip(query, p) if a != b)
            if hamming < min_hamming:
                min_hamming = hamming
                best_match = {"label": l, "hamming_distance": hamming}
        return {
            "corrupted_query": corrupted,
            "best_match": best_match,
            "grover_iterations": 2,
            "retrieval_complexity": "O(sqrt(N))"
        }

class QuantumInterferenceDecisionEngine:
    def evaluate_options(self, options):
        # Mock logic
        for opt in options:
            opt["score"] = opt["utility"] * opt["evidence_strength"] - opt["risk"]
        options.sort(key=lambda x: x["score"], reverse=True)
        return {
            "recommended": options[0]["name"],
            "confidence": 0.85,
            "ranking": options
        }

    def multi_step_decision(self, tree):
        path = []
        conf = 1.0
        for i, step_options in enumerate(tree):
            decision = self.evaluate_options(step_options)
            conf *= decision["confidence"]
            path.append({
                "step": i,
                "chosen": decision["recommended"],
                "cumulative_confidence": conf
            })
        return {
            "decision_path": path,
            "final_confidence": conf
        }

class QuantumTensorNetworkAttention:
    def __init__(self, embed_dim, bond_dim):
        self.embed_dim = embed_dim
        self.bond_dim = bond_dim

    def compute_attention(self, tokens):
        # Mock matrix
        n = len(tokens)
        matrix = [[round(0.1 + 0.8 * (i == j), 2) for j in range(n)] for i in range(n)]
        return {
            "embed_dim": self.embed_dim,
            "bond_dim": self.bond_dim,
            "entanglement_capacity": "High",
            "cross_attention_strength": "MPS-based",
            "compression_ratio": self.embed_dim / self.bond_dim,
            "attention_matrix": matrix
        }

class QRAM:
    def __init__(self, address_qubits):
        self.address_qubits = address_qubits
        self.capacity = 2 ** address_qubits
        self.memory = {}

    def write(self, address, data):
        self.memory[address] = data

    def quantum_read(self, superposition):
        results = []
        for addr, amp in superposition:
            if addr in self.memory:
                results.append({"address": addr, "data": self.memory[addr], "probability": abs(amp)**2})
        return {
            "router_depth": "O(log(N))",
            "access_time": "O(log(N))",
            "classical_equivalent_time": "O(N)",
            "speedup": "Exponential",
            "results": results
        }

class QuantumLanguageModel:
    def __init__(self, vocab_size, embed_dim, bond_dim, context_qubits):
        self.vocab_size = vocab_size
        self.embed_dim = embed_dim
        self.bond_dim = bond_dim
        self.context_qubits = context_qubits

    def load_context(self, tokens):
        return {
            "tokens_loaded": len(tokens),
            "context_window": 2 ** self.context_qubits,
            "qubits_used": self.context_qubits
        }

    def attend(self, query):
        return {
            "model_type": "Quantum MPS Attention",
            "advantages": ["Exponential Context", "Logarithmic Routing"]
        }


# Re-export phase 34 production implementations.
from .qml import (
    QuantumAssociativeMemory,
    QuantumInterferenceDecisionEngine,
    QuantumTensorNetworkAttention,
    QRAM,
    QuantumLanguageModel,
)
