Pyqrack

Latest version: v1.27.8

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1.24.1

This release improves and debugs `QBdtHybrid`. Additionally, this is the first release of PyQrack that uses Qrack v9 API, corresponding to new build targets.

SHA-256 sums:
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5bd7ee2188420c4fe8053319995082d48d4852ef26c5f707758f6a0137b339c3 pyqrack-1.24.1-py3-none-linux_aarch64.whl
50c52529e4eb445bd8af5625cfa858192256b7fa1fc5d1aab9061ecd51fcd602 pyqrack-1.24.1-py3-none-linux_armv7l.whl
4e4fccbf5fcdd012de6ca483d4bfe3e09ace2bcfc258a1cae5d8edd305609578 pyqrack-1.24.1-py3-none-macosx_10_4_x86_64.whl
2894dbed889aa34f43944ad36afcb151c268c76f1c78998de6ebd1021bc370bf pyqrack-1.24.1-py3-none-manylinux2014_x86_64.whl
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da20528b66580f9fad7b6a9d6883190009b6281e0fd675cf0fe64493ab2bcae5 pyqrack-1.24.1-py3-none-win_amd64.whl
fabdae8b30faec68f9d1df3ea0deb1a33f7585ad7d34b18e9cc0093ecce1cd10 pyqrack-1.24.1.tar.gz

1.24.0

While we've tried various "hybridizations" of quantum binary decision diagrams (QBDD) with state vector simulation and other methods in the past, they as of yet have not given any compelling practical advantages in general use cases, as best the Qrack team can assess.

Trying a slightly different approach with the new QBdtHybrid, this might remain the case, like for the quantum Fourier transform or random circuit sampling. However, holistic integration with the overall "layer stack" seems significantly improved, with this approach, and special-case advantages might have improved. (We still need to collect additional benchmark data, to test this.)

File SHA-1 sums:
cda0a18756f8a003fc1c224d0bc105943d7b9fb1 pyqrack-1.24.0-py3-none-any.whl
c3ffd1fd5772c9c2389fd2b499c79a81cc968118 pyqrack-1.24.0-py3-none-linux_aarch64.whl
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31809acfa00b87ca914d9f3ae209c5db774feb97 pyqrack-1.24.0-py3-none-win32.whl
7991aa8136263fa22b1be660347c3d88a1def546 pyqrack-1.24.0-py3-none-win_amd64.whl
c171fcaf9e1311ec454c2a4bafedb5feba9f2851 pyqrack-1.24.0.tar.gz

1.23.1

This patch release debugs `QUnitMulti` for heterogeneous OpenCL devices. **Note that this is the first ever release with CMake `-DUINTPOW=5` option, hence you will need to recompile your OpenCL kernels, if you use "pre-compilation."** `-DUINTPOW=5` limits `QPager` "page" width to 31 qubits or less, but this is literally larger than the maximum allocation segment of an A100 GPU, and this has no impact on multi-GPU scaling, hence there should be no new limitations so long as environment variable `QRACK_MAX_PAGE_QB` is set to `31` or less, (and most systems, including the A100, will typically want to set this variable even _lower._)

With this release, interoperability between NVIDIA GPU and Intel HD integrated graphics can offer significant speed advantages on quantum volume circuits, for example. For a gaming laptop with two OpenCL devices, an Intel HD (device number `0`) and an RTX 3080 (device number `1`), here is an example of how the environment variable might be set:

export QRACK_OCL_DEFAULT_DEVICE=1
export QRACK_MAX_PAGE_QB=27
export QRACK_QUNITMULTI_DEVICES=1,0
export QRACK_QUNITMULTI_DEVICES_MAX_QB=-1,15
export QRACK_QPAGER_DEVICES=8.1,24.0
export QRACK_QPAGER_DEVICES_HOST_POINTER=8.0,24.1
export QRACK_MAX_ALLOC_MB=24576,16108
export QRACK_MAX_PAGING_QB=32
export QRACK_MAX_CPU_QB=32
export QRACK_QTENSORNETWORK_THRESHOLD_QB=32
These commented variables might also be tuned for performance and approximation
export QRACK_PSTRIDEPOW=11
export QRACK_QUNIT_SEPARABILITY_THRESHOLD=0.00001
export QRACK_QBDT_SEPARABILITY_THRESHOLD=0.00001
export QRACK_NONCLIFFORD_ROUNDING_THRESHOLD=1


File SHA-1 sums:
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26099f2c626d412c14785095596a2aa173fabaca pyqrack-1.23.1-py3-none-linux_aarch64.whl
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68f2d661593aa65b9aacc7ac4d54be37bc73b4e5 pyqrack-1.23.1-py3-none-win_amd64.whl
fc60129a0c20be898469076a89e146ef919c3553 pyqrack-1.23.1.tar.gz

1.23.0

This feature release adds the `QRACK_QUNITMULTI_DEVICES_MAX_QB` environment variable. Per device, as listed and ordered in `QRACK_QUNITMULTI_DEVICES`, this new variable allows the user to specify a maximum subsystem qubit width, above which no larger subsystem will be allocated on the particular device. This is particularly useful for combining OpenCL CPUs with OpenCL GPUs and other accelerators, since CPUs can give a significant advantage but are best suited for lower parallelism widths than GPUs.

File SHA-1 sums:
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079d1b332d267943e584796ee2daf9a951923082 pyqrack-1.23.0-py3-none-linux_aarch64.whl
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d07f28a6abe312325fd331af0b9843c22c0b6392 pyqrack-1.23.0-py3-none-win_amd64.whl
a40990c51eae76d66a6c84fbff792d92271fc444 pyqrack-1.23.0.tar.gz

1.22.0

QrackCircuit` now exposes a `bool` constructor options that, if `true`, only "simplifies" Clifford-with-Clifford and non-Clifford-with-non-Clifford gates, but not Clifford-with-non-Clifford. `QTensorNetwork` automatically leverages this option when `QStabilizerHybrid` is in use, as in default "layer stack" case. (Some circuits still might complete more efficiently without `QStabilizerHybrid`.)

File SHA-1 sums:
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058c12cf0cc9906d5dd599bf6e8cfbb8df5df119 pyqrack-1.22.0-py3-none-linux_aarch64.whl
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17bbea4729be8d02bc8f9bc7b40765b68d44f932 pyqrack-1.22.0.tar.gz

1.21.4

For CPU-based and OpenCL-based back ends, a minor improvement has been made in the algorithm of MAll(), such that single-shot simulation might see a small average-case performance benefit.

Also, Qrack's intention was always that internal "epsilon" for n bit precision was 2^-(n+1); these values were accidentally set much lower. (The change has been tested with the "normalization" features of Qrack, which are basically all this change should affect, and it works.)

File SHA-1 sums:
a9ae91e05a5f3e2c70f303c22a9b411faae4da7d pyqrack-1.21.4-py3-none-any.whl
1ad84872c22fdb50a64ca5b18d594d4dbd3571e6 pyqrack-1.21.4-py3-none-linux_aarch64.whl
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417809d2006bb1277d4590560ecd49fec11da6a2 pyqrack-1.21.4.tar.gz

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