Python objects & constants

Objects

Network(subscripts, *operands[, qualifiers, ...])	Create a tensor network object specified as an Einstein summation expression.
BaseCUDAMemoryManager(*args, **kwargs)	Protocol for memory manager plugins.
MemoryPointer(device_ptr, size, finalizer)	An RAII class for a device memory buffer.
CircuitToEinsum(circuit[, dtype, backend])	Create a converter object that can generate Einstein summation expressions and tensor operands for a given circuit.
NetworkOptions([compute_type, device_id, ...])	A data class for providing options to the cuquantum.Network object.
OptimizerInfo(largest_intermediate, ...)	A data class for capturing optimizer information.
OptimizerOptions([samples, threads, path, ...])	A data class for providing options to the cuTensorNet optimizer.
PathFinderOptions([num_partitions, ...])	A data class for capturing the path finder options.
ReconfigOptions([num_iterations, num_leaves])	A data class for capturing the reconfiguration options.
SlicerOptions([disable_slicing, ...])	A data class for capturing the slicer options.

Enums

Here is a collection of enums supported by both custatevec and cutensornet modules.

Warning

In a future release, enums exposed here will be deprecated and removed in favor of those imported from CUDA Python.

ComputeType(value)	An enumeration of CUDA compute types.
cudaDataType(value)	An enumeration of cudaDataType_t.
libraryPropertyType(value)	An enumeration of library version information.

Python functions

contract(subscripts, *operands[, options, ...])	Evaluate the Einstein summation convention on the operands.
contract_path(subscripts, *operands[, ...])	Evaluate the "best" contraction order by allowing the creation of intermediate tensors.
einsum(subscripts, *operands[, out, dtype, ...])	A drop-in replacement of numpy.einsum() for computing the specified tensor contraction using cuTensorNet.
einsum_path(subscripts, *operands[, optimize])	A drop-in replacement of numpy.einsum_path() for evaluating the "best" contraction order using cuTensorNet.

Command Line Support

The outputs of python -m cuquantum can be used in your build system to help discover the location of binaries used by cuQuantum Python.

usage: __main__.py [-h] [--includes] [--libs]
                   [--target {custatevec,cutensornet}]

options:
  -h, --help            show this help message and exit
  --includes            get cuQuantum include flags
  --libs                get cuQuantum linker flags
  --target {custatevec,cutensornet}
                        get the linker flag for the target cuQuantum component