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new 35a8526d9 [Bug][QDP] Align Iris GPU training path with sibling
pipelines (real dtype + leaf weights) (#1412)
35a8526d9 is described below
commit 35a8526d9cb08669ee3989b801be74bea0216cf4
Author: Ryan Huang <[email protected]>
AuthorDate: Fri Jul 10 14:14:16 2026 +0900
[Bug][QDP] Align Iris GPU training path with sibling pipelines (real dtype
+ leaf weights) (#1412)
---
.../encoding_benchmarks/qdp_pipeline/iris_amplitude.py | 17 +++++++++++------
1 file changed, 11 insertions(+), 6 deletions(-)
diff --git
a/qdp/qdp-python/benchmark/encoding_benchmarks/qdp_pipeline/iris_amplitude.py
b/qdp/qdp-python/benchmark/encoding_benchmarks/qdp_pipeline/iris_amplitude.py
index 7b0132343..09ba096f1 100644
---
a/qdp/qdp-python/benchmark/encoding_benchmarks/qdp_pipeline/iris_amplitude.py
+++
b/qdp/qdp-python/benchmark/encoding_benchmarks/qdp_pipeline/iris_amplitude.py
@@ -322,9 +322,12 @@ def _run_training_gpu(
) -> dict[str, Any]:
"""GPU path: lightning.gpu + PyTorch interface, data stays on GPU.
Optional early stop every 100 steps."""
device = encoded_train.device
- dtype = encoded_train.dtype
- Y_train_t = torch.tensor(Y_train, dtype=dtype, device=device)
- Y_test_t = torch.tensor(Y_test, dtype=dtype, device=device)
+ # Encoded data may be complex (from QDP); use real dtype for weights, bias
and labels.
+ real_dtype = (
+ torch.float64 if encoded_train.dtype == torch.complex128 else
torch.float32
+ )
+ Y_train_t = torch.tensor(Y_train, dtype=real_dtype, device=device)
+ Y_test_t = torch.tensor(Y_test, dtype=real_dtype, device=device)
@qml.qnode(dev_qml, interface="torch", diff_method="adjoint")
def circuit(weights, state_vector):
@@ -341,10 +344,12 @@ def _run_training_gpu(
return torch.mean((Y_batch - preds) ** 2)
torch.manual_seed(seed)
- weights = 0.01 * torch.randn(
- num_layers, NUM_QUBITS, 3, device=device, dtype=dtype,
requires_grad=True
+ weights = (
+ (0.01 * torch.randn(num_layers, NUM_QUBITS, 3, device=device,
dtype=real_dtype))
+ .detach()
+ .requires_grad_(True)
)
- bias = torch.tensor(0.0, device=device, dtype=dtype, requires_grad=True)
+ bias = torch.tensor(0.0, device=device, dtype=real_dtype,
requires_grad=True)
opt = torch.optim.SGD([weights, bias], lr=lr, momentum=0.9, nesterov=True)
t0 = time.perf_counter()
