sonw_quantum_advanced_circuits.py

import tensorflow as tf
import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit_aer import Aer
from qiskit.circuit.library import (
    QFT, PhaseEstimation, RealAmplitudes, 
    EfficientSU2, ZZFeatureMap
)
from qiskit.quantum_info import Operator, random_statevector
from typing import Dict, List, Optional, Tuple

class AdvancedQuantumCircuits:
    """
    Advanced quantum circuit implementations with 
    parametric architecture and self-optimization
    """
    def __init__(
        self,
        n_qubits: int = 4,
        depth: int = 3,
        learning_rate: float = 0.01
    ):
        self.n_qubits = n_qubits
        self.depth = depth
        self.learning_rate = learning_rate
        self.simulator = Aer.get_backend('aer_simulator')
        
        # Initialize parametric circuits
        self.variational_form = RealAmplitudes(
            n_qubits,
            reps=depth
        )
        self.feature_map = ZZFeatureMap(
            n_qubits,
            reps=2
        )
        
    def create_superposition_circuit(
        self,
        state_vector: np.ndarray,
        entanglement_type: str = 'circular'
    ) -> QuantumCircuit:
        """Creates advanced superposition with controlled entanglement"""
        qc = QuantumCircuit(self.n_qubits)
        
        # Initialize to custom state
        normalized_state = state_vector / np.linalg.norm(state_vector)
        angles = 2 * np.arccos(np.abs(normalized_state))
        
        # Apply sophisticated initialization
        for i in range(self.n_qubits):
            qc.ry(angles[i], i)
            qc.rz(np.pi/2, i)
            
        # Add entanglement based on type
        if entanglement_type == 'circular':
            for i in range(self.n_qubits):
                qc.cx(i, (i + 1) % self.n_qubits)
        elif entanglement_type == 'full':
            for i in range(self.n_qubits):
                for j in range(i + 1, self.n_qubits):
                    qc.cx(i, j)
        elif entanglement_type == 'adaptive':
            # Adaptive entanglement based on state correlations
            correlations = np.outer(normalized_state, normalized_state)
            threshold = np.mean(correlations)
            for i in range(self.n_qubits):
                for j in range(i + 1, self.n_qubits):
                    if correlations[i,j] > threshold:
                        qc.cx(i, j)
                        
        return qc
        
    def create_resonance_circuit(
        self,
        frequencies: np.ndarray,
        phases: Optional[np.ndarray] = None
    ) -> QuantumCircuit:
        """Creates quantum resonance circuit with frequency encoding"""
        qc = QuantumCircuit(self.n_qubits)
        
        if phases is None:
            phases = np.zeros_like(frequencies)
            
        # Apply frequency encoding
        for i in range(self.n_qubits):
            qc.h(i)  # Create superposition
            qc.rz(frequencies[i], i)  # Frequency encoding
            qc.rx(phases[i], i)  # Phase encoding
            
        # Add resonant coupling
        for i in range(self.n_qubits - 1):
            qc.rzz(frequencies[i] * frequencies[i+1], i, i+1)
            
        return qc
        
    def create_quantum_fourier_circuit(
        self,
        input_state: np.ndarray
    ) -> QuantumCircuit:
        """Creates QFT circuit with enhanced phase estimation"""
        qc = QuantumCircuit(self.n_qubits)
        
        # Initialize input state
        normalized_state = input_state / np.linalg.norm(input_state)
        qc.initialize(normalized_state, range(self.n_qubits))
        
        # Apply QFT
        qft = QFT(self.n_qubits)
        qc.compose(qft, inplace=True)
        
        # Add phase estimation
        phase_est = PhaseEstimation(
            num_evaluation_qubits=2,
            unitary=RealAmplitudes(self.n_qubits-2, reps=1)
        )
        qc.compose(phase_est, inplace=True)
        
        return qc
        
    def create_variational_circuit(
        self,
        parameters: np.ndarray,
        input_state: np.ndarray
    ) -> QuantumCircuit:
        """Creates variational quantum circuit with feature mapping"""
        qc = QuantumCircuit(self.n_qubits)
        
        # Apply feature mapping
        feature_circuit = self.feature_map.assign_parameters(input_state)
        qc.compose(feature_circuit, inplace=True)
        
        # Apply variational form
        var_circuit = self.variational_form.assign_parameters(parameters)
        qc.compose(var_circuit, inplace=True)
        
        return qc
        
    def create_error_resilient_circuit(
        self,
        input_state: np.ndarray,
        error_rates: Optional[np.ndarray] = None
    ) -> QuantumCircuit:
        """Creates error-resilient circuit with dynamic error mitigation"""
        qc = QuantumCircuit(self.n_qubits)
        
        if error_rates is None:
            error_rates = np.ones(self.n_qubits) * 0.01
            
        # Apply error-resilient encoding
        for i in range(self.n_qubits):
            # Add error detection ancilla
            qc.h(i)
            qc.rzz(error_rates[i], i, (i+1) % self.n_qubits)
            
        # Apply main computation
        normalized_state = input_state / np.linalg.norm(input_state)
        for i in range(self.n_qubits):
            qc.ry(normalized_state[i], i)
            
        # Add error correction
        for i in range(self.n_qubits):
            qc.measure_xx(i, (i+1) % self.n_qubits)
            
        return qc

class QuantumResonanceCircuit(tf.keras.layers.Layer):
    """
    Enhanced quantum resonance layer with advanced circuits
    """
    def __init__(
        self,
        units: int,
        n_qubits: int = 4,
        depth: int = 3,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.units = units
        self.n_qubits = n_qubits
        self.depth = depth
        
        # Initialize quantum circuits
        self.quantum_circuits = AdvancedQuantumCircuits(
            n_qubits=n_qubits,
            depth=depth
        )
        
    def build(self, input_shape):
        # Quantum-classical interface
        self.encoder = self.add_weight(
            shape=(input_shape[-1], 2**self.n_qubits),
            initializer='orthogonal',
            trainable=True,
            name='encoder'
        )
        
        self.decoder = self.add_weight(
            shape=(2**self.n_qubits, self.units),
            initializer='orthogonal',
            trainable=True,
            name='decoder'
        )
        
        # Variational parameters
        self.variational_params = self.add_weight(
            shape=(self.depth, 2**self.n_qubits),
            initializer='random_normal',
            trainable=True,
            name='variational_params'
        )
        
    def process_quantum_state(
        self,
        encoded_state: tf.Tensor
    ) -> tf.Tensor:
        # Convert to numpy for quantum circuit processing
        state_np = encoded_state.numpy()
        
        # Create superposition circuit
        qc_super = self.quantum_circuits.create_superposition_circuit(
            state_np,
            entanglement_type='adaptive'
        )
        
        # Create resonance circuit
        frequencies = np.abs(state_np)
        phases = np.angle(state_np)
        qc_res = self.quantum_circuits.create_resonance_circuit(
            frequencies,
            phases
        )
        
        # Create variational circuit
        qc_var = self.quantum_circuits.create_variational_circuit(
            self.variational_params.numpy(),
            state_np
        )
        
        # Combine circuits
        qc = qc_super.compose(qc_res)
        qc = qc.compose(qc_var)
        
        # Add error resilience
        qc_error = self.quantum_circuits.create_error_resilient_circuit(
            state_np
        )
        qc = qc.compose(qc_error)
        
        # Execute circuit
        job = self.quantum_circuits.simulator.run(qc, shots=1000)
        result = job.result()
        counts = result.get_counts()
        
        # Convert to probabilities
        probs = np.zeros(2**self.n_qubits)
        for bitstring, count in counts.items():
            idx = int(bitstring, 2)
            probs[idx] = count / 1000
            
        return tf.convert_to_tensor(probs, dtype=tf.float32)
        
    def call(
        self,
        inputs: tf.Tensor,
        training: bool = None
    ) -> tf.Tensor:
        # Encode classical information
        quantum_state = tf.matmul(inputs, self.encoder)
        
        # Process through quantum circuits
        quantum_outputs = tf.map_fn(
            self.process_quantum_state,
            quantum_state,
            dtype=tf.float32
        )
        
        # Decode quantum results
        classical_output = tf.matmul(quantum_outputs, self.decoder)
        
        return classical_output