torchqml 0.1.3

PyTorch Quantum Machine Learning with cuQuantum - Fast Adjoint Differentiation

torchqml

PyTorch Quantum Machine Learning with cuQuantum - Fast Adjoint Differentiation

Features

• Adjoint Differentiation: O(G) gradient calculation using cuQuantum's custatevec and optimized C++ kernels.
• PyTorch Native: Fully integrated with torch.autograd and torch.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)