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Updates the requirements on qiskit, qiskit-ibm-runtime, qiskit-machine-learning, azure-quantum, torch, torchvision, 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 numpy to 2.5.2

Release notes

Sourced from numpy's releases.

v2.5.2 (Aug 9, 2026)

NumPy 2.5.2 Release Notes

The NumPy 2.5.2 is a patch release that fixes bugs discovered after the 2.5.1 release. The big news is that it includes wheels for the newly released Python 3.15.0rc1.

This release supports Python versions 3.12-3.15

C API changes

PyArray_StringDTypeObject is opaque under the abi3t stable ABI

The PyArray_StringDTypeObject was accidentally exposed in NumPy 2.5 when targeting the free-threading-compatible stable ABI (Py_TARGET_ABI3T). PyArray_StringDTypeObject is now an opaque struct: extensions compiled that way cannot access its fields, since the struct layout depends on the size of the object header. Any code that accessed PyArray_StringDTypeObject fields in an abi3t build would have crashed, so we are making this API change in a bugfix release.

The NpyString allocator API remains usable by passing the descriptor object pointer, e.g. NpyString_acquire_allocator((PyArray_StringDTypeObject *)descr).

(gh-31771)

Contributors

A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

  • Abhijeetsingh Meena +
  • Charalampos Stratakis
  • Charles Harris
  • Chris Ninham +
  • David Woods
  • Geonho +
  • Gopu Yeshwanth Reddy +
  • Iason Krommydas
  • Ijtihed Kilani
  • Jelle Zijlstra +
  • Joren Hammudoglu
  • Kumar Aditya
  • Mike Boyle
  • Nathan Goldbaum
  • Raghuveer Devulapalli
  • Sebastian Berg

... (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
  • 48fecee REL: Prepare for the NumPy 2.5.2 release (#32226)
  • ecf599c Merge pull request #32221 from charris/backport-32151
  • 3c7ac97 Merge pull request #32220 from charris/backport-32205
  • 23b30f4 BUG: avoid segfaults when legacy copyswap slot is not defined (#32151)
  • 4964ca8 TYP: isclose shape-typing fix for 2d array-likes (#32205)
  • c37ed94 MAINT: Skip limited_api tests on some platforms. (#32214)
  • 5cfd73b Merge pull request #32206 from charris/update-cibuildwheel
  • d8262bc MAINT: Update cibuildwheel to v4.2.0
  • 988d94d Merge pull request #32158 from charris/backport-32133
  • b2e4f97 BUG: avoid possible stack overflow in arraydescr_dealloc (#32133)
  • 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 Merge pull request #32051 from meeseeksmachine/auto-backport-of-pr-32037-on-v...
  • c624aec Backport PR #32038: Fix occasional misalignment in reported mouse position (a...
  • 8eda2b7 Backport PR #32037: Bump the actions group with 3 updates
  • a0e7ae5 Backport PR #31304: Fix restoring 'auto' aspect in 3D axes after switching fr...
  • 9001323 Merge pull request #32047 from meeseeksmachine/auto-backport-of-pr-32025-on-v...
  • Additional commits viewable in compare view

Updates plotly to 7.0.0

Release notes

Sourced from plotly's releases.

v7.0.0

Fixed

  • Fix hex_to_rgb parsing of 3-digit shorthand hexadecimal colors such as #FFF [#5662], with thanks to @​genrichez for the contribution!
  • Improve px.*_map() auto-fitting behavior when zoom and center are not specified [#5686]
  • Add <!doctype html> to the to_html() template to comply with modern web standards [#5693], with thanks to @​mishrakushal for the contribution!
  • Apply histfunc/z aggregation to marginal_x/marginal_y="histogram" subplots in density_heatmap/density_contour, instead of always showing raw bin counts [#3521], with thanks to @​lucasjamar for the contribution!
  • Fix mpl_to_plotly silently dropping matplotlib path collections in data coordinates (such as violin plots, pcolor, event plots, stack plots, fill_between, and stem plots) by rendering them as filled polygons or lines [#5702], with thanks to @​robertoffmoura for the contribution!

Removed

  • Remove the deprecated Figure Factory functions create_2d_density, create_annotated_heatmap, create_bullet, create_candlestick, create_choropleth, create_distplot, create_facet_grid, create_g...

    Description has been truncated

@dependabot
deps(lora-quantum-ai): bump the lora-quantum-ai-deps group across 1 d…
d9e31d9 
…irectory with 15 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), [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 `numpy` to 2.5.2
- [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...v2.5.2)

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: numpy
  dependency-version: 2.5.2
  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 7, 2026 04:19
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dependabot Bot commented on behalf of github Sep 14, 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 14, 2026
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dependabot Bot deleted the dependabot/pip/ai-projects/lora-training/quantum-ai/lora-quantum-ai-deps-1a97a8c2ce branch September 14, 2026 04:16
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