Release Notes

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.