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import numpy as np
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from typing import Optional, Dict, Any, List
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from quantum_topology import ChernSimonsTopology
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from quantum_gates import QuantumGate
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class QuantumCircuit:
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def __init__(self, topology: ChernSimonsTopology):
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self.topology = topology
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self.gates: List[QuantumGate] = []
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self.initialize_gates()
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def initialize_gates(self):
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"""Initialize quantum gates based on topology"""
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self.gates = [
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QuantumGate("H"),
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QuantumGate("CNOT"),
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QuantumGate("Phase"),
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QuantumGate("X"),
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QuantumGate("Z")
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]
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def prepare_input(self, data: str) -> np.ndarray:
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"""Convert classical input to quantum state"""
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state = np.zeros(self.topology.dimension, dtype=np.complex128)
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state[0] = 1.0
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for i, char in enumerate(data):
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if i >= self.topology.depth:
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break
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if ord(char) % 2:
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h_gate = QuantumGate("H")
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state = h_gate.apply(state, self.topology)
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state /= np.sqrt(np.sum(np.abs(state) ** 2))
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return state
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def evolve(
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self,
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state: np.ndarray,
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params: Optional[Dict[str, Any]] = None
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) -> np.ndarray:
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current_state = state.copy()
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if params and "gates" in params:
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for gate_name in params["gates"]:
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gate = QuantumGate(gate_name)
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current_state = gate.apply(current_state, self.topology)
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else:
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for gate in self.gates:
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current_state = gate.apply(current_state, self.topology)
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for i in range(self.topology.depth - 1):
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for j in range(i + 1, self.topology.depth):
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braiding = self.topology.calculate_braiding(i, j)
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current_state = np.dot(current_state.reshape(-1, 4), braiding.T).flatten()
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current_state /= np.sqrt(np.sum(np.abs(current_state) ** 2))
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return current_state |
