Merge pull request #272 from AbdullahKazi500/AbdullahKazi500-patch-3

torchquantum/pulse/quantum_pulse_simulation.py

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+import torch
+import torch.optim as optim
+import torchquantum as tq
+import torchquantum.functional as tqf
+import numpy as np
+
+class QuantumPulseDemo(tq.QuantumModule):
+    """
+    Quantum pulse demonstration module.
+
+    This module defines a parameterized quantum pulse and applies it to a quantum device.
+    """
+
+    def __init__(self):
+        """
+        Initializes the QuantumPulseDemo module.
+
+        Args:
+            n_wires (int): Number of quantum wires (qubits).
+            n_steps (int): Number of steps for the quantum pulse.
+            hamil (list): Hamiltonian for the quantum pulse.
+        """
+        super().__init__()
+        self.n_wires = 2
+
+        # Quantum encoder
+        self.encoder = tq.GeneralEncoder([
+            {'input_idx': [0], 'func': 'rx', 'wires': [0]},
+            {'input_idx': [1], 'func': 'rx', 'wires': [1]}
+        ])
+
+        # Define parameterized quantum pulse
+        self.pulse = tq.pulse.QuantumPulseDirect(n_steps=4, hamil=[[0, 1], [1, 0]])
+
+    def forward(self, x):
+        """
+        Forward pass through the QuantumPulseDemo module.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Measurement result from the quantum device.
+        """
+        qdev = tq.QuantumDevice(n_wires=self.n_wires, bsz=x.shape[0], device=x.device)
+        self.encoder(qdev, x)
+        self.apply_pulse(qdev)
+        return tq.measure(qdev)
+
+    def apply_pulse(self, qdev):
+        """
+        Applies the parameterized quantum pulse to the quantum device.
+
+        Args:
+            qdev (tq.QuantumDevice): Quantum device to apply the pulse to.
+        """
+        pulse_params = self.pulse.pulse_shape.detach().cpu().numpy()
+        # Apply pulse to the quantum device (adjust based on actual pulse application logic)
+        for params in pulse_params:
+            tqf.rx(qdev, wires=0, params=params)
+            tqf.rx(qdev, wires=1, params=params)
+
+class OM_EOM_Simulation:
+    """
+    Optical modulation with electro-optic modulator (EOM) simulation.
+
+    This class simulates a sequence of optical pulses with or without EOM modulation.
+    """
+
+    def __init__(self, pulse_duration, modulation_bandwidth=None, eom_mode=False):
+        """
+        Initializes the OM_EOM_Simulation.
+
+        Args:
+            pulse_duration (float): Duration of each pulse.
+            modulation_bandwidth (float, optional): Bandwidth of modulation. Defaults to None.
+            eom_mode (bool, optional): Whether to simulate EOM mode. Defaults to False.
+        """
+        self.pulse_duration = pulse_duration
+        self.modulation_bandwidth = modulation_bandwidth
+        self.eom_mode = eom_mode
+
+    def simulate_sequence(self):
+        """
+        Simulates a sequence of optical pulses with specified parameters.
+
+        Returns:
+            list: Sequence of pulses and delays.
+        """
+        # Initialize the sequence
+        sequence = []
+
+        # Add pulses and delays to the sequence
+        if self.modulation_bandwidth:
+            # Apply modulation bandwidth effect
+            sequence.append(('Delay', 0))
+            sequence.append(('Pulse', 'NoisyChannel'))
+        for _ in range(3):
+            # Apply pulses with specified duration
+            sequence.append(('Delay', self.pulse_duration))
+            if self.eom_mode:
+                # Apply EOM mode operation
+                sequence.append(('Pulse', 'EOM'))
+            else:
+                # Apply regular pulse
+                sequence.append(('Pulse', 'Regular'))
+            # Apply a delay between pulses
+            sequence.append(('Delay', 0))
+
+        return sequence
+
+class QuantumPulseDemoRunner:
+    """
+    Runner for training the QuantumPulseDemo model and simulating the OM_EOM_Simulation.
+    """
+
+    def __init__(self, pulse_duration, modulation_bandwidth=None, eom_mode=False):
+        """
+        Initializes the QuantumPulseDemoRunner.
+
+        Args:
+            pulse_duration (float): Duration of each pulse.
+            modulation_bandwidth (float, optional): Bandwidth of modulation. Defaults to None.
+            eom_mode (bool, optional): Whether to simulate EOM mode. Defaults to False.
+        """
+        self.model = QuantumPulseDemo()
+        self.optimizer = optim.Adam(params=self.model.pulse.parameters(), lr=5e-3)
+        self.target_unitary = self._initialize_target_unitary()
+        self.simulator = OM_EOM_Simulation(pulse_duration, modulation_bandwidth, eom_mode)
+
+    def _initialize_target_unitary(self):
+        """
+        Initializes the target unitary matrix.
+
+        Returns:
+            torch.Tensor: Target unitary matrix.
+        """
+        theta = 0.6
+        return torch.tensor(
+            [
+                [np.cos(theta / 2), -1j * np.sin(theta / 2)],
+                [-1j * np.sin(theta / 2), np.cos(theta / 2)],
+            ],
+            dtype=torch.complex64,
+        )
+
+    def train(self, epochs=1000):
+        """
+        Trains the QuantumPulseDemo model.
+
+        Args:
+            epochs (int, optional): Number of training epochs. Defaults to 1000.
+        """
+        for epoch in range(epochs):
+            x = torch.tensor([[np.pi, np.pi]], dtype=torch.float32)
+
+            qdev = self.model(x)
+
+            loss = (
+                1
+                - (
+                    torch.trace(self.model.pulse.get_unitary() @ self.target_unitary)
+                    / self.target_unitary.shape[0]
+                ).abs()
+                ** 2
+            )
+
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+
+            if epoch % 100 == 0:
+                print(f'Epoch {epoch}, Loss: {loss.item()}')
+                print('Current Pulse Shape:', self.model.pulse.pulse_shape)
+                print('Current Unitary:\n', self.model.pulse.get_unitary())
+
+    def run_simulation(self):
+        """
+        Runs the OM_EOM_Simulation.
+        """
+        sequence = self.simulator.simulate_sequence()
+        for step in sequence:
+            print(step)
+
+# Example usage
+runner = QuantumPulseDemoRunner(pulse_duration=100, modulation_bandwidth=5, eom_mode=True)
+runner.train()
+runner.run_simulation()