| |
| import json |
| import sys |
| from pathlib import Path |
|
|
| import torch |
|
|
|
|
| PROJECT_DIR = Path(__file__).resolve().parents[2] |
| sys.path.insert(0, str(PROJECT_DIR / "code/src")) |
|
|
| from quantum_qwen38.quantum_residual import NativeStatevectorVQC |
|
|
|
|
| def main() -> None: |
| torch.manual_seed(20260828) |
| device = torch.device("cuda:0") |
|
|
| angles = (torch.rand(32, 8, device=device) * 2.0 - 1.0).requires_grad_(True) |
| analytic_circuit = NativeStatevectorVQC(n_qubits=8, depth=0).to(device) |
| measured = analytic_circuit(angles) |
| expected = torch.cos(angles) |
| forward_max_abs_error = float((measured - expected).abs().max()) |
| measured.sum().backward() |
| gradient_max_abs_error = float((angles.grad - (-torch.sin(angles.detach()))).abs().max()) |
|
|
| trainable_circuit = NativeStatevectorVQC(n_qubits=8, depth=1).to(device) |
| fixed_angles = torch.randn(16, 8, device=device) |
| loss = trainable_circuit(fixed_angles).square().mean() |
| loss.backward() |
| autograd_value = float(trainable_circuit.theta.grad[0, 0, 0]) |
|
|
| epsilon = 1.0e-3 |
| with torch.no_grad(): |
| original = trainable_circuit.theta[0, 0, 0].clone() |
| trainable_circuit.theta[0, 0, 0] = original + epsilon |
| plus = float(trainable_circuit(fixed_angles).square().mean()) |
| trainable_circuit.theta[0, 0, 0] = original - epsilon |
| minus = float(trainable_circuit(fixed_angles).square().mean()) |
| trainable_circuit.theta[0, 0, 0] = original |
| finite_difference_value = (plus - minus) / (2.0 * epsilon) |
| finite_difference_abs_error = abs(autograd_value - finite_difference_value) |
| finite_difference_relative_error = finite_difference_abs_error / max( |
| abs(autograd_value), abs(finite_difference_value), 1.0e-12 |
| ) |
|
|
| result = { |
| "status": "ok", |
| "device": torch.cuda.get_device_name(0), |
| "analytic_forward_max_abs_error": forward_max_abs_error, |
| "analytic_input_gradient_max_abs_error": gradient_max_abs_error, |
| "trainable_gate_autograd": autograd_value, |
| "trainable_gate_finite_difference": finite_difference_value, |
| "trainable_gate_abs_error": finite_difference_abs_error, |
| "trainable_gate_relative_error": finite_difference_relative_error, |
| "passes": { |
| "forward": forward_max_abs_error < 1.0e-5, |
| "input_gradient": gradient_max_abs_error < 1.0e-5, |
| "trainable_gate_gradient": finite_difference_relative_error < 2.0e-2, |
| }, |
| } |
| if not all(result["passes"].values()): |
| result["status"] = "failed" |
| print(json.dumps(result, ensure_ascii=False, indent=2)) |
| if result["status"] != "ok": |
| raise SystemExit(1) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
|
|
