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April 26, 2024 07:57
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import numpy as np | |
from numba import cuda | |
# Define Pauli matrices | |
sigma_x = np.array([[0, 1], [1, 0]], dtype=np.complex128) | |
sigma_z = np.array([[1, 0], [0, -1]], dtype=np.complex128) | |
# Define Hadamard gate | |
hadamard = (1 / np.sqrt(2)) * np.array([[1, 1], [1, -1]], dtype=np.complex128) | |
# Define CNOT gate | |
cnot = np.array([[1, 0, 0, 0], | |
[0, 1, 0, 0], | |
[0, 0, 0, 1], | |
[0, 0, 1, 0]], dtype=np.complex128) | |
@cuda.jit | |
def bell_state_kernel(qubits, out): | |
tx = cuda.threadIdx.x | |
ty = cuda.blockIdx.x | |
bw = cuda.blockDim.x | |
idx = tx + ty * bw | |
if idx < out.size: | |
# Apply Hadamard gate to first qubit | |
qubits[idx][0] = hadamard[0, 0] * qubits[idx][0] + hadamard[0, 1] * qubits[idx][1] | |
qubits[idx][1] = hadamard[1, 0] * qubits[idx][0] + hadamard[1, 1] * qubits[idx][1] | |
# Apply CNOT gate | |
qubits[idx][2] = qubits[idx][0] * cnot[0, 0] + qubits[idx][1] * cnot[0, 1] + qubits[idx][2] * cnot[0, 2] + qubits[idx][3] * cnot[0, 3] | |
qubits[idx][3] = qubits[idx][0] * cnot[1, 0] + qubits[idx][1] * cnot[1, 1] + qubits[idx][2] * cnot[1, 2] + qubits[idx][3] * cnot[1, 3] | |
# Reset qubits 0 and 1 | |
qubits[idx][0] = 0 | |
qubits[idx][1] = 0 | |
out[idx][0] = qubits[idx][2] | |
out[idx][1] = qubits[idx][3] | |
# Main function to call the CUDA kernel | |
def apply_bell_state_on_gpu(qubits): | |
# Allocate memory on the device | |
d_qubits = cuda.to_device(qubits.astype(np.complex128)) | |
d_out = cuda.device_array_like(qubits) | |
# Configure the kernel | |
threads_per_block = 256 | |
blocks_per_grid = (qubits.shape[0] + (threads_per_block - 1)) // threads_per_block | |
# Launch the kernel | |
bell_state_kernel[blocks_per_grid, threads_per_block](d_qubits, d_out) | |
# Copy the result back to the host | |
return d_out.copy_to_host() | |
# Example usage | |
N = 2 | |
qubits = np.array([[1, 0] for _ in range(N)], dtype=np.complex128) | |
print("Initial qubits:") | |
print(qubits) | |
result = apply_bell_state_on_gpu(qubits) | |
print(result) |
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