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deps(lora-quantum-ai): bump the lora-quantum-ai-deps group across 1 directory with 16 updates - #1210

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Updates the requirements on qiskit, qiskit-ibm-runtime, qiskit-machine-learning, azure-quantum, torch, torchvision, scikit-learn, numpy, pandas, matplotlib, plotly, jupyterlab, ipywidgets, python-dotenv, tqdm and mypy to permit the latest version.
Updates qiskit to 2.5.2

Release notes

Sourced from qiskit's releases.

Qiskit 2.5.2

Changelog

Fixed

  • Sum VF2Layout average errors in a deterministic order (#16732) (#16761)
  • Fix add/sub optimizations including the same symbols (backport #16752) (#16759)
  • Reimplement SymbolExpr::eval to be iterative (backport #16756) (#16758)
  • dagcircuit: transfer DAG-level variables in substitute_node_with_dag (#15542) (#16744)
  • Fix memory leak in Suzuki-Trotter synthesis (#16687) (#16739)
  • Fix OptimizeCliffordT producing non-unitarily-equivalent output when basis excludes sx/sxdg (#16729) (#16735)
  • Parameter expression bugfixes (backport #16283) (#16714)
  • Update to rsgridsynth 0.2.2 (backport #16685) (#16711)
  • Fix potential panic in DAGCircuit.depth by bumping rustworkx-core to 0.18.1 (#16686) (#16715)
  • Preserve StatePreparation width in inverse (#16431) (#16681)
  • Fix circuit drawing layer overlaps inside of ControlFlowOps (#16639) (#16659)
  • Fix non-determinism in DAGCircuit::remove_qubits (#16656) (#16658)
Commits

Updates qiskit-ibm-runtime to 0.49.0

Release notes

Sourced from qiskit-ibm-runtime's releases.

Qiskit Ibm Runtime 0.49.0

Upgrade Notes

  • The option classes from the :mod:options_models package are now based on Pydantic's BaseModel instead of on dataclass. (#3042)
  • Switched default schema version for Executor to 2.0. (#3043)
  • The Qiskit Runtime service has been renamed to IBM Quantum Compute service, and the references to it in the documentation have been updated. The :class:~.QiskitRuntimeService class is now available as IBMQuantumComputeService. (#3070)

Deprecation Notes

  • The usage of stdlib asdict with option classes from the :mod:options_models package is now deprecated, as they are now Pydantic models. Instead, use options.model_dump() directly for serializing them to a dict. (#3042)
  • The post_selection option :class:.NoiseLearnerV3 is now deprecated and will be removed in a future release. (#3160)

New Features

  • Added :class:~qiskit_ibm_runtime.circuit.library.MidCircuitReset instruction and :class:~qiskit_ibm_runtime.transpiler.passes.ConvertToMidCircuitResetAndMeasure transpiler pass. The new pass extends the existing :class:~qiskit_ibm_runtime.transpiler.passes.ConvertToMidCircuitMeasure functionality by also replacing non-terminal reset instructions with hardware-specific mid-circuit reset implementations. (#2903)
  • Added bit_flip_checks field to NoiseLearnerV3Options, allowing users to request bit-flip checks to the results of noise learning circuits. (#2907)
  • Added an optional persist parameter to :meth:.FakeBackendV2.refresh. By default (persist=True) the behavior is unchanged: the refreshed configuration and properties overwrite the data files bundled with the installed package so the update persists across sessions. Passing persist=False instead writes the refreshed data to a temporary directory, which allows :meth:~.FakeBackendV2.refresh to succeed even when the package is installed in a read-only location (for example, a system-wide site-packages directory) where overwriting the bundled files would otherwise raise a PermissionError. In that mode, the backend is updated for the current session only and the changes are not persisted to disk. (#2977)
  • :class:~.IBMBackend now has a physical_qubits attribute, which returns the number of physical qubits for the backend (programmable qubits, reset qubits, and couplers combined). The attribute is also available in :class:~.FakeBackendV2 for backends that are not retired. (#3080)
  • The IAM URL used by :meth:.QiskitRuntimeService.instances can now be overridden by the IAM_URL environment variable, following the same convention as the IBM SDK's GLOBAL_SEARCH_URL and GLOBAL_CATALOG_URL variables. This allows routing IAM traffic through a private endpoint (for example, https://private.iam.cloud.ibm.com) in VPE environments without any code changes. (#3135)

Bug Fixes

... (truncated)

Commits
  • c4b4b51 Add a simple e2e test for Executor-based Sampler that submits jobs via local-...
  • 1a4aad9 Prepare 0.49.0 release (#3187)
  • 4cefb18 Add tests to verify the structure of the template circuit after prepare in th...
  • a464e89 Move as_array out of main loop in wrapper estimator utils (#3195)
  • 8027fee Added integration test for executor-based estimator, PEC path (#3189)
  • fcbacfa Fix passthrough data handling by excluding noise model mapping from o… (#3188)
  • c276ca8 Add shape parameter to create_pub_result_pea and create_pub_result_zn… (#3185)
  • 44cd2a7 Implement noise factors validation for ZNE preparation (#3161)
  • 1acaa5d Add executor local mode support to estimator (#3184)
  • a48c2cd Add (noiseless) executor local mode support to wrapper sampler (#3175)
  • Additional commits viewable in compare view

Updates qiskit-machine-learning to 0.9.1

Release notes

Sourced from qiskit-machine-learning's releases.

v0.9.1

Qiskit Machine Learning 0.9.1

Qiskit Machine Learning 0.9.1 includes new regression-oriented kernel loss functions, improvements to QNN behavior and numerical robustness, a new quantum dataset generator, compatibility fixes, and updates to development and CI infrastructure.

Highlights

  • Added regression-based kernel loss functions, including SVRLoss and MSRLoss, together with convenient string aliases.
  • Added a Phase of Matter quantum dataset generator.
  • Fixed SamplerQNN._postprocess handling for transpiled circuit layouts.
  • Fixed numerical issues in EffectiveDimension Fisher information calculations for non-positive outputs.
  • Improved support for multidimensional sparse COO data.
  • Deprecated automatic qubit alignment.
  • Updated typing, linting, formatting, security, and CI tooling.

What's Changed

New Contributors

... (truncated)

Commits
  • c97b5ed Set version to 0.9.1
  • 3bdcce7 Fix: Types errors with new version of mypy (#1063)
  • 8609b0d Bump actions/setup-python from 6 to 7 (#1052)
  • c6ae27d Removed unnecessary warnings to make QNNs consistent with other algorithms. (...
  • b651c7d Added AI policy and instructions for agents. (Issue #1057) (#1058)
  • cfcdb65 Bump coverallsapp/github-action from 2.3.7 to 2.3.8 (#1053)
  • 5b5a221 Fix CI by adding explicit casts to scalars coming from multidimensional COO (...
  • 94c12da Bump actions/checkout from 6 to 7 (#1049)
  • 725c7c8 Add Phase of Matter quantum dataset generator (#1044)
  • f683284 Fix SamplerQNN._postprocess to handle transpiled circuit layouts (#1041)
  • Additional commits viewable in compare view

Updates azure-quantum to 3.12.0

Release notes

Sourced from azure-quantum's releases.

azure-quantum_v3.12.0

What's Changed

Full Changelog: microsoft/azure-quantum-python@azure-quantum_v3.11.0...azure-quantum_v3.12.0

Commits
  • e7c9490 Update network isolation policy (#770)
  • 4640862 Add Job.list_attachments() to enumerate a job's output container blobs (#768)
  • 82cced9 Fix typo in signal-processing.ipynb (#769)
  • 713004b Allow version to optionally be manually set during release (#767)
  • b25df2c Add update job operation to support updating name, priority and tags (#764)
  • e1689fd Support full-result format in Pasqal Result (#762)
  • cae08e6 Query Azure Artifacts Feed for Existing Version when Determining Built Versio...
  • 3f08ead Add the isolation policy to publish pipeline (#760)
  • eefaef0 Update tox Environments to Explicitly Use Azure Feed (#759)
  • 3eb01e9 Add support for job delete operation (#758)
  • Additional commits viewable in compare view

Updates torch to 2.14.0

Release notes

Sourced from torch's releases.

PyTorch 2.14.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

torch.nn

  • torch.nn.LinearCrossEntropyOptions no longer accepts acc_policy="balanced"; use "compact" instead (#188283)

    The "balanced" policy was removed because "compact" provides the same weight-gradient accumulation precision with lower memory use on CUDA, already uses the equivalent scratch layout for mixed-precision inputs on other devices, and was never selected by "auto". Constructing the options with acc_policy="balanced" now raises ValueError: invalid acc_policy: 'balanced'; expected one of 'auto', 'accurate', 'compact'.

    Before:

    options = torch.nn.LinearCrossEntropyOptions(acc_policy="balanced")
loss = torch.nn.functional.linear_cross_entropy(
    input, linear_weight, target, options=options
)

    After:

    options = torch.nn.LinearCrossEntropyOptions(acc_policy="compact")
loss = torch.nn.functional.linear_cross_entropy(

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits
  • 2b3ec34 [release/2.14] Import SDPAParams in test_transformers to fix lint (#194970)
  • 08187d9 [cuDNN] Add guards for cuDNN SDPA decode (#194963)
  • 8ceea97 Pin cython < 3.3.0 for the Windows Triton wheel build (#194931)
  • 99ecebc [Cherry-pick][release/2.14] [inductor] Fix loop-local load CSE lifetime (#194...
  • ec283a7 Bump the Python 3.15 numpy pin to 2.5.2 (#194821)
  • 65890f3 Fix docker-release validate job to use the channel matching the pushed image ...
  • 1682388 Fix macOS py3.15 wheel builds: pin Cython < 3.3.0 and bump the cp315 numpy pi...
  • 9724418 Fix Windows py3.15 builds: constrain Cython < 3.3.0 and bump the cp315 numpy ...
  • f1b7554 [MPS] Fix pin_memory() recycling buffers still in use by the GPU (#194662)
  • 9f205f7 [MPS] fail loudly on large reductions (#194661)
  • Additional commits viewable in compare view

Updates torchvision to 0.29.0

Release notes

Sourced from torchvision's releases.

TorchVision 0.29: ABI stability!

TorchVision 0.29 is out! It comes with two major changes: ABI stability, and deprecation of the image decoders and encoders (now in TorchCodec)!

ABI Stability with torch 2.14

TorchVision is now ABI stable w.r.t. torch 2.14! This means that torchvision 0.29 will be compatible with future versions of torch: 2.15, 2.16, etc. You won’t need to install a new version of TorchVision when you upgrade torch.

As a result, we might stop releasing TorchVision in sync with pytorch. But TorchVision is still actively maintained and developed: we’ll still be pushing releases, just not with the same cadence.

Thanks to Adrian Abeyta @​adabeyta for the fantastic porting effort!

PRs: #9524, #9597, #9598, #9605, #9612, #9610, #9614, #9584, #9617, #9618, #9619, #9620, #9582, #9573, #9625, #9623, #9626, #9583, #9633, #9572, #9549, #9533, #9535, #9539, #9543, #9550, #9552, #9555, #9557, #9558, #9554

Image decoders and encoders are deprecated. Use TorchCodec!

The image decoders and encoders in torchvision.io are now deprecated, and they will be removed in a future release. They are now available in torchcodec >= 0.16, where they are significantly more capable. You’ll just need to pip install torchcodec, and you can refer to this migration guide for migrating your code (most APIs for decoding are the same).

This finalizes a clear separation of concerns for the three media-processing libraries of PyTorch: torchcodec is for decoding and encoding all media (images, videos, and audio), while torchvision and torchaudio focus on the transforms.

Bug fixes

[ops] Fix for deformable convolution kernels always running on default stream (#9522) [ops, MPS] Fix gradient overaccumulation in ROI ops (#9563, #9510) [transforms] Fix JPEG transform for non-contiguous batches (#9615)

Contributors

🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Adrian Abeyta, Andrey Talman, Dmitry Nikolaev, Irakli Salia, Jeff Daily , Kasra Ghodsi, Nicolas Hug, Nikita Shulga, Simon Byrne, Yutao Xu, Zhewen

Commits
  • fc73f5a Merge branch 'release/0.29' of github.com:pytorch/vision into release/0.29
  • f9b2669 [Cherry-pick for 0.29] (#9634)
  • e1b5802 Revert "Update version.txt for 0.30.0 (#9607)"
  • 2757376 Merge remote-tracking branch 'origin' into release/0.29
  • 9e21b88 Consolidate CUDA helpers and remove the legacy _C extension in place of Stabl...
  • 541c083 Port deform_conv2d (CUDA) to stable ABI. (#9626)
  • 2f411e7 Port ps_roi_pool (CUDA) to stable ABI. (#9623)
  • ae0ca92 Port ps_roi_align (CUDA) to stable ABI. (#9625)
  • ff1a18d Port roi_align (CUDA) to stable ABI. (#9573)
  • 8af7a68 Port roi_pool (CUDA) to stable ABI. (#9582)
  • Additional commits viewable in compare view

Updates scikit-learn to 1.9.1

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.9.1

We're happy to announce the 1.9.1 release.

This release contains a few bug fixes and is the first version supporting Python 3.15.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.9.html#version-1-9-1

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Thanks to everyone who contributed to this release !

Commits
  • 866c0f5 generate changelog
  • 12f135f update upper bounds
  • 43ff2f2 bump version
  • b851524 DOC Mark dev index as orphan (#34916)
  • 01d56d4 DOC Fix wikipedia principal eigenvector example references (#34915)
  • b86a1e2 🔒 🤖 CI Update lock files for array-api CI build(s) 🔒 🤖 ...
  • d282698 🔒 🤖 CI Update lock files for main CI build(s) 🔒 🤖 (#34897)
  • eb34279 🔒 🤖 CI Update lock files for free-threaded CI build(s) 🔒 :rob...
  • 7581303 FIX: Fix QuantileTransformer(ignore_implicit_zeros=True) sub-sampling behav...
  • ac47f4d FIX: avoid EfficiencyWarning in OPTICS with metric='precomputed' (#34692)
  • Additional commits viewable in compare view

Updates numpy to 2.5.3

Release notes

Sourced from numpy's releases.

v2.5.3 (Sep 6, 2026)

NumPy 2.5.3 Release Notes

The NumPy 2.5.3 is a patch release that fixes bugs discovered after the 2.5.2 release. Apart from the usual bug and maintenance work, there are a number of StringDType related fixes for problems discovered during the ongoing string work in the main branch.

This release supports Python versions 3.12-3.15

Changes

  • Casting a fixed-width byte string array (np.bytes_) to StringDType now raises TypeError when the bytes are not valid UTF-8. Previously the invalid bytes were stored as-is and later caused undefined behavior in string operations.

    (gh-32296)

  • MaskedArray._fill_value would become stale when ufuncs that change dtype left the result holding a fill_value typed for the old dtype. The mismatch was silent until something later called _check_fill_value, such as .view(), and then a TypeError would be raised. Now, when the copied fill_value is no longer valid for the new dtype, fall back to the default fill_value for that dtype instead of propagating the stale value. This may raise a ComplexWarning if the fill_value is complex and the new dtype is real.

    (gh-32423)

Contributors

A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

  • Charles Harris
  • Iason Krommydas
  • James Davies +
  • Joren Hammudoglu
  • Maanas Arora
  • Matti Picus
  • Nathan Goldbaum
  • Shikhar Goel +
  • Yeonho Kim +

Pull requests merged

A total of 27 pull requests were merged for this release.

  • #32235: MAINT: Prepare 2.5.x for further development

... (truncated)

Changelog

Sourced from numpy's changelog.

This is a walkthrough of the NumPy 2.4.0 release on Linux, which will be the first feature release using the numpy/numpy-release <https://github.com/numpy/numpy-release>__ repository.

The commands can be copied into the command line, but be sure to replace 2.4.0 with the correct version. This should be read together with the :ref:general release guide <prepare_release>.

Facility preparation

Before beginning to make a release, use the requirements/*_requirements.txt files to ensure that you have the needed software. Most software can be installed with pip, but some will require apt-get, dnf, or whatever your system uses for software. You will also need a GitHub personal access token (PAT) to push the documentation. There are a few ways to streamline things:

  • Git can be set up to use a keyring to store your GitHub personal access token. Search online for the details.

Prior to release

Add/drop Python versions

When adding or dropping Python versions, multiple config and CI files need to be edited in addition to changing the minimum version in pyproject.toml. Make these changes in an ordinary PR against main and backport if necessary. We currently release wheels for new Python versions after the first Python RC once manylinux and cibuildwheel support that new Python version.

Backport pull requests

Changes that have been marked for this release must be backported to the maintenance/2.4.x branch.

Update 2.4.0 milestones

Look at the issues/prs with 2.4.0 milestones and either push them off to a later version, or maybe remove the milestone. You may need to add a milestone.

Check the numpy-release repo

... (truncated)

Commits
  • dd88c0c Merge pull request #32511 from charris/prepare-2.5.3
  • edcac6a REL: Prepare for the NumPy 2.5.3 release
  • fd4d908 Merge pull request #32509 from charris/backport-32496
  • 65bb1da BUG: fix crash in ufunc.resolve_dtypes with a Python scalar type (#32496)
  • 294956e Merge pull request #32506 from charris/backport-32503
  • 26428d9 DOC: fix scipy docs links in intersphinx mapping (#32507)
  • 5fab1cb DOC: use static scipy doc site for intershpinx (#32503)
  • 7beed2f Merge pull request #32481 from ngoldbaum/stringdtype-backport
  • 8972f70 Merge pull request #32478 from charris/backport-32466
  • ab1b589 Merge pull request #32477 from charris/backport-32423
  • Additional commits viewable in compare view

Updates pandas to 3.0.5

Release notes

Sourced from pandas's releases.

pandas 3.0.5

We are pleased to announce the release of pandas 3.0.5. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version.

See the full whatsnew for a list of all the changes.

Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI:

python -m pip install --upgrade pandas==3.0.*

Or from conda-forge

conda install -c conda-forge pandas=3.0

Please report any issues with the release on the pandas issue tracker.

Thanks to all the contributors who made this release possible.

Commits

Updates matplotlib to 3.11.1

Release notes

Sourced from matplotlib's releases.

REL: v3.11.1

This is the first bugfix release of the 3.11.x series.

This release contains several bug-fixes and adjustments:

  • Fix tight layout of multiple subplots with sharey=True
  • Fix NoNorm cursor formatting for uint8 images
  • Fix occasional misalignment in reported mouse position (also fix a bug with canvas height)
  • Fix clipped Axis labels on 3D plots with tight layout
  • Fix inverted Axis on 3D plots
  • Fix restoring 'auto' aspect in 3D axes after switching from 'equal'
  • Fix missing glyphs when subsetting Type 1 fonts in PDF
  • Fix oversized embedding of Type 42 fonts in PDF/PostScript files

As well as several documentation and typing improvements and corrections.

Commits
  • 3c1757a REL: v3.11.1
  • 792c6b5 DOC: Prepare for 3.11.1
  • aaff9f6 Merge branch 'v3.11.0-doc' into v3.11.x
  • d4f4527 Merge pull request #32052 from meeseeksmachine/auto-backport-of-pr-32038-on-v...
  • 6ad4bcf Merge pull request #32050 from meeseeksmachine/auto-backport-of-pr-31304-on-v...
  • 7fb74d3 Mer...

    Description has been truncated

@dependabot
deps(lora-quantum-ai): bump the lora-quantum-ai-deps group across 1 d…
6c875be 
…irectory with 16 updates

Updates the requirements on [qiskit](https://github.com/Qiskit/qiskit), [qiskit-ibm-runtime](https://github.com/Qiskit/qiskit-ibm-runtime), [qiskit-machine-learning](https://github.com/qiskit-community/qiskit-machine-learning), [azure-quantum](https://github.com/microsoft/azure-quantum-python), [torch](https://github.com/pytorch/pytorch), [torchvision](https://github.com/pytorch/vision), [scikit-learn](https://github.com/scikit-learn/scikit-learn), [numpy](https://github.com/numpy/numpy), [pandas](https://github.com/pandas-dev/pandas), [matplotlib](https://github.com/matplotlib/matplotlib), [plotly](https://github.com/plotly/plotly.py), [jupyterlab](https://github.com/jupyterlab/jupyterlab), [ipywidgets](https://github.com/jupyter-widgets/ipywidgets), [python-dotenv](https://github.com/theskumar/python-dotenv), [tqdm](https://github.com/tqdm/tqdm) and [mypy](https://github.com/python/mypy) to permit the latest version.

Updates `qiskit` to 2.5.2
- [Release notes](https://github.com/Qiskit/qiskit/releases)
- [Changelog](https://github.com/Qiskit/qiskit/blob/main/docs/release_notes.rst)
- [Commits](Qiskit/qiskit@2.5.0...2.5.2)

Updates `qiskit-ibm-runtime` to 0.49.0
- [Release notes](https://github.com/Qiskit/qiskit-ibm-runtime/releases)
- [Commits](Qiskit/qiskit-ibm-runtime@0.47.0...0.49.0)

Updates `qiskit-machine-learning` to 0.9.1
- [Release notes](https://github.com/qiskit-community/qiskit-machine-learning/releases)
- [Commits](qiskit-community/qiskit-machine-learning@0.9.0...0.9.1)

Updates `azure-quantum` to 3.12.0
- [Release notes](https://github.com/microsoft/azure-quantum-python/releases)
- [Commits](microsoft/azure-quantum-python@azure-quantum_v3.10.0...azure-quantum_v3.12.0)

Updates `torch` to 2.14.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@ciflow/torchtitan/125995...v2.14.0)

Updates `torchvision` to 0.29.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](pytorch/vision@v0.28.0...v0.29.0)

Updates `scikit-learn` to 1.9.1
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.9.0...1.9.1)

Updates `numpy` to 2.5.3
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v2.5.1...v2.5.3)

Updates `pandas` to 3.0.5
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Commits](pandas-dev/pandas@v3.0.4...v3.0.5)

Updates `matplotlib` to 3.11.1
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](matplotlib/matplotlib@v3.11.0...v3.11.1)

Updates `plotly` to 7.0.0
- [Release notes](https://github.com/plotly/plotly.py/releases)
- [Changelog](https://github.com/plotly/plotly.py/blob/main/CHANGELOG.md)
- [Commits](plotly/plotly.py@v6.9.0...v7.0.0)

Updates `jupyterlab` to 4.6.3
- [Release notes](https://github.com/jupyterlab/jupyterlab/releases)
- [Changelog](https://github.com/jupyterlab/jupyterlab/blob/main/RELEASE.md)
- [Commits](https://github.com/jupyterlab/jupyterlab/compare/@jupyterlab/lsp@4.6.1...@jupyterlab/lsp@4.6.3)

Updates `ipywidgets` to 8.1.9
- [Release notes](https://github.com/jupyter-widgets/ipywidgets/releases)
- [Commits](jupyter-widgets/ipywidgets@8.1.8...8.1.9)

Updates `python-dotenv` to 1.2.3
- [Release notes](https://github.com/theskumar/python-dotenv/releases)
- [Changelog](https://github.com/theskumar/python-dotenv/blob/main/CHANGELOG.md)
- [Commits](theskumar/python-dotenv@v1.2.2...v1.2.3)

Updates `tqdm` to 4.70.0
- [Release notes](https://github.com/tqdm/tqdm/releases)
- [Commits](tqdm/tqdm@v4.68.4...v4.70.0)

Updates `mypy` to 2.3.1
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](python/mypy@v2.2.0...v2.3.1)

---
updated-dependencies:
- dependency-name: qiskit
  dependency-version: 2.5.2
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: qiskit-ibm-runtime
  dependency-version: 0.49.0
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: qiskit-machine-learning
  dependency-version: 0.9.1
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: azure-quantum
  dependency-version: 3.12.0
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: torch
  dependency-version: 2.14.0
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: torchvision
  dependency-version: 0.29.0
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: scikit-learn
  dependency-version: 1.9.1
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: numpy
  dependency-version: 2.5.3
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: pandas
  dependency-version: 3.0.5
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: matplotlib
  dependency-version: 3.11.1
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: plotly
  dependency-version: 7.0.0
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: jupyterlab
  dependency-version: 4.6.3
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: ipywidgets
  dependency-version: 8.1.9
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: python-dotenv
  dependency-version: 1.2.3
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: tqdm
  dependency-version: 4.70.0
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
- dependency-name: mypy
  dependency-version: 2.3.1
  dependency-type: direct:production
  dependency-group: lora-quantum-ai-deps
...

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dependabot Bot requested a review from Bryan-Roe as a code owner September 14, 2026 04:23
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dependabot Bot commented on behalf of github Sep 28, 2026

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Looks like these dependencies are updatable in another way, so this is no longer needed.

@dependabot dependabot Bot closed this Sep 28, 2026
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dependabot Bot deleted the dependabot/pip/ai-projects/lora-training/quantum-ai/lora-quantum-ai-deps-77d70c37d8 branch September 28, 2026 04:12
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