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