torchqml
PyTorch Quantum Machine Learning with cuQuantum - Fast Adjoint Differentiation
Features
- Adjoint Differentiation: O(G) gradient calculation using cuQuantum's
custatevecand optimized C++ kernels. - PyTorch Native: Fully integrated with
torch.autogradandtorch.nn. - High Performance: Custom C++/CUDA extension reusing cuStateVec/cuBLAS handles for minimize overhead.
Performance
TorchQML outperforms PennyLane's lightning.gpu backend significantly on NVIDIA GPUs (measured on T4):
| Qubits | Layers | TorchQML (ms) | PennyLane (ms) | Speedup |
|---|---|---|---|---|
| 4 | 5 | 5.72 | 16.99 | 3.0x |
| 8 | 10 | 13.18 | 48.69 | 3.7x |
| 12 | 10 | 24.29 | 69.65 | 2.9x |
Installation
pip install .
Usage
import torch
import torchqml as tq
# Build circuit
qc = tq.QuantumCircuit(2)
qc.h(0)
qc.ry(0, param_index=0)
qc.cx(0, 1)
# Parameters
params = torch.tensor([[0.5]], requires_grad=True)
# Expectation
exp_val = qc.expectation(params, tq.Z(0))
# Backward
exp_val.backward()
print(params.grad)
1 maintainer
Metadata
1 maintainer
Release files for torchqml 0.1.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| torchqml-0.1.7.tar.gz | 58.3 kB | Details |
Release files / torchqml-0.1.7.tar.gz
| Download URL | torchqml-0.1.7.tar.gz |
|---|---|
| Size | 58.3 kB |
| Tags | Source |
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