Add zero-copy GPU state vector access (#836) - #1100
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This PR adds an opt-in zero-copy API, `QSimSimulator.simulate_into_device_array(...)`, to extract the final state vector from GPU simulation without copying data from GPU to host memory (issue quantumlib#836). The returned `DeviceStateVector` object owns the GPU allocation and exposes it through the CUDA Array Interface (`__cuda_array_interface__`, v3), allowing downstream GPU frameworks (CuPy, PyTorch, Numba) to consume the device buffer directly without a device -> host -> device round trip.
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Code Review
This pull request implements zero-copy device state-vector bindings, allowing GPU-based simulations to retain their final state in device memory and expose it via the CUDA Array Interface for direct consumption by libraries like CuPy. Feedback on these changes highlights a potential out-of-bounds read vulnerability when passing non-contiguous NumPy arrays as initial states, which can be resolved by ensuring C-contiguity. Additionally, it is recommended to use std::make_unique instead of direct new expressions when instantiating std::unique_ptrs to comply with the Google C++ Style Guide.
- Ensure initial_state NumPy array is C-contiguous (via np.ascontiguousarray) before creating float32 view to prevent out-of-bounds reads on non-contiguous array slices. - Use std::make_unique<DeviceStateVector> instead of bare new expression per Google C++ Style Guide. - Add test_cirq_qsim_gpu_simulate_into_device_array_with_non_contiguous_input_state test case.
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This PR adds an opt-in zero-copy API,
QSimSimulator.simulate_into_device_array(...), to extract the final state vector from GPU simulation without copying data from GPU to host memory (issue #836).The returned
DeviceStateVectorobject owns the GPU allocation and exposes it through the CUDA Array Interface (__cuda_array_interface__, v3), allowing downstream GPU frameworks (CuPy, PyTorch, Numba) to consume the device buffer directly without a device -> host -> device round trip.