Release Notes

cuQuantum Python v22.11.0

• We are on NVIDIA/cuQuantum GitHub Discussions! For any questions regarding (or exciting works built upon) cuQuantum, please feel free to reach out to us on GitHub Discussions.

  • Bug reports should still go to our GitHub issue tracker.

• Add new APIs and functionalities:

  • For cuTensorNet low-level APIs, please refer to the release notes of cuTensorNet v2.0.0; no new cuStateVec API is added.

  • A new API, cuquantum.CircuitToEinsum.batched_amplitudes() to compute the amplitudes for a batch of qubits. This is equivalent to the kwargs fixed support in cuquantum.CircuitToEinsum.state_vector(), which is deprecated and will be removed in a future release.

  • A new API, cuquantum.CircuitToEinsum.expectation() to support expectation value computation for Pauli strings.

  • A new helper API, cuquantum.cutensornet.get_mpi_comm_pointer() to get the pointer to and size of MPI communicator for the new low-level API cuquantum.cutensornet.distributed_reset_configuration() that enables disbtributed parallelism. This capability requires mpi4py.

  • The cuquantum module now has a command line interface to return the include and library paths and the linker flags for the cuTENSOR and cuQuantum libraries: python -m cuquantum. See Command Line Support for detail.

  • Support for controlling non-blocking behavior via the new option cuquantum.NetworkOptions.blocking.

• API changes:

  • For cuTensorNet low-level APIs, please refer to the release notes of cuTensorNet v2.0.0; cuStateVec low-level APIs remain unchanged.

    • Users can set the tensor qualifiers by using the dedicated NumPy dtype cuquantum.cutensornet.tensor_qualifiers_dtype. See the Python sample (python/samples/cutensornet/coarse/example21.py) for details. For example, complex conjugation can be done on-the-fly, reducing memory pressure.

    • To get/set the contraction path or slicing configurations, users should use contraction_optimizer_info_get_attribute_dtype() to get a NumPy custom dtype representing the path or slicing configuration object, in a manner consistent with the method used for all other attributes. Refer to the docstring for details. The (experimental) ContractionPath object is removed.

• Functionality/performance improvements:

  • Improved performance when contracting two tensors using cuquantum.contract() or related APIs.

  • Improved performance for reusing a Network object with reset_operands().

  • The lightcone construction in CircuitToEinsum is improved to further reduce the number of tensors in the network.

  • The build system now supports PEP-517 and standard pip command-line flags. The environment variable CUQUANTUM_IGNORE_SOLVER is no longer used. See Getting Started for more information.

• Bugs fixed:

  • Fix a potential multi-device bug in the internal device context switch.

  • Fix a bug for using invalid mode labels in cuquantum.CircuitToEinsum when input circuit size gets large.

• Other changes:

  • Provide one more distributed (MPI+NCCL) Python sample (example4_mpi_nccl.py) to show how to use cuTensorNet and create parallelism.

  • The test infrastructure will show tests that are not runnable as “deselected” instead of “skipped”.

  • A new pip wheel is released on PyPI: pip install cuquantum-python-cu11. Users can still install cuQuantum Python via pip install cuquantum-python, as before. cuquantum-python now becomes a meta-wheel pointing to cuquantum-python-cu11. This may change in a future release when a new CUDA version becomes available. Using wheels with the -cuXX suffix is encouraged.

Compatibility notes:

• cuQuantum Python now requires cuStateVec 1.1.0 or above.

• cuQuantum Python now requires cuTensorNet 2.0.0 or above.

• cuQuantum Python now requires cuTENSOR 1.6.1 or above.

cuQuantum Python v22.07.1

• Bugs fixed:

  • The 22.07.0 cuquantum wheel had a wrong file layout. (If you are using the cuqauntum 22.07.0.1 or 22.07.0.2 hot-fix wheel, they will work fine.)

cuQuantum Python v22.07.0

• Add new APIs and functionalities:

  • For low-level APIs, please refer to the release notes of cuStateVec v1.1.0 and cuTensorNet v1.1.0.

  • New high-level API cuquantum.CircuitToEinsum that supports conversion of qiskit.QuantumCircuit and cirq.Circuit to tensor network contraction:

    • Support state coefficient

    • Support bitstring amplitude

    • Support reduced density matrix

    • Backend support on NumPy, CuPy and PyTorch

  • Add a keyword-only argument slices to the cuquantum.Network.contract() method to support contracting an arbitrary subset of the slices.

  • Add a new attribute intermediate_modes to the cuquantum.OptimizerInfo object for retrieving the mode labels of all intermediate tensors.

  • Add a new attribute num_slices to the cuquantum.OptimizerInfo object for querying the total number of slices.

• Functionality/performance improvements:

  • Improve the einsum expression parser.

• Bugs fixed:

  • An exception mistakenly raised in cuquantum.einsum() when optimize is set to False.

  • Missing f-specifier in the string representation of cuquantum.OptimizerInfo.

• Other changes:

  • Drop the dependency on typing_extensions.

  • Provide distributed (MPI-based) Python samples that show how easy it is to use cuTensorNet and create parallelism. mpi4py is required for running these samples.

  • Update the low-level, non-distributed sample tensornet_example.py by improving memory usage and switching to the new contraction API contract_slices().

  • Provide Jupyter notebooks to show how to convert a quantum circuit to a tensor network contraction.

  • Add a Python sample to illustrate the usage of the new multi-device bit-swapping multi_device_swap_index_bits() API.

  • Restructure the samples folder to separate cuStateVec and cuTensorNet samples.

Compatibility notes:

• cuQuantum Python now requires cuQuantum v22.07.

• cuQuantum Python now requires Python 3.8+.

• cuQuantum Python now requires NumPy v1.19+.

• cuQuantum Python supports Cirq v0.6.0+.

• cuQuantum Python supports Qiskit v0.24.0+.

cuQuantum Python v22.05.0

• Bugs fixed:

  • Make typing_extensions a required dependency (NVIDIA/cuQuantum#3)

  • Fix issues in the test suite

• Other changes:

  • The Python sample (python/samples/tensornet_example.py) is updated to include a correctness check

cuQuantum Python v22.03.0

• Stable release:

  • Starting this release, cuQuantum Python switches to the CalVer versioning scheme, following cuQuantum SDK

  • pip wheels are released on PyPI: pip install cuquantum-python

• Functionality/performance improvements:

  • High-level tensor network APIs are now fully NumPy compliant:

    • Support generalized einsum expressions

    • Support ellipsis

    • Support broadcasting

• Add new APIs and functionalities for:

  • For low-level APIs, please refer to the release notes of cuStateVec v1.0.0 and cuTensorNet v1.0.0.

  • The high-level APIs support an EMM-like memory plugin interface (see Memory management).

• API changes:

  • For low-level APIs, please refer to the release notes of cuStateVec v1.0.0 and cuTensorNet v1.0.0.

  • No API breaking changes for the high-level APIs.

Compatibility notes:

• cuQuantum Python requires cuQuantum v22.03

• cuQuantum Python requires Python 3.7+

  • In the next release, Python 3.7 will be dropped to follow NEP-29.

• cuQuantum Python requires NumPy v1.17+

  • In the next release, NumPy 1.17 & 1.18 will be dropped to follow NEP-29.

• cuQuantum Python requires CuPy v9.5+

• cuQuantum Python supports PyTorch v1.10+

Known issues:

• If you install cuQuantum Python from PyPI (pip install cuquantum-python), make sure you also install typing_extensions (via pip or conda). This only affects the wheel installation and will be fixed in the next release. (NVIDIA/cuQuantum#3)

cuQuantum Python v0.1.0.1

• Patch release:

  • Add a __version__ string

cuQuantum Python v0.1.0.0

• Initial release (beta 2)

Compatibility notes:

• cuQuantum Python requires cuQuantum v0.1.0

• cuQuantum Python requires NumPy v1.17+

• cuQuantum Python requires CuPy v9.5+

• cuQuantum Python supports PyTorch v1.10+

Limitation notes:

• In certain environments, if PyTorch is installed import cuquantum could fail (with a segmentation fault). It is currently under investigation and a temporary workaround is to import torch before importing cuquantum.