| | 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
|
| |
|
| | 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 |
