Add support for TensorFlow 2.19

[psamanoelton]

Summary

Update TensorFlow Quantum build to support TensorFlow 2.19.1.

Keep TFQ aligned with TensorFlow 2.19.1’s Bazel and Python repository expectations, confirm Eigen remains on TensorFlow’s pinned revision, and regenerate Python dependencies from requirements.in.

Key changes

WORKSPACE

• bump @org_tensorflow to 2.19.1
• add python_version_repo to satisfy TensorFlow 2.19’s expected Python version repository layout
• add lightweight wrapper lockfiles (requirements_lock_3_10.txt, requirements_lock_3_11.txt, requirements_lock_3_12.txt) so the same compiled requirements can be reused across the supported Python versions
• confirm Eigen remains aligned with TensorFlow 2.19.1’s pinned revision

requirements.in / requirements.txt

• update TensorFlow requirement to 2.19.1
• update tf-keras requirement to 2.19.x
• regenerate requirements.txt from requirements.in with pip-compile under Python 3.10
• keep the dependency set compatible with Python 3.10, 3.11, and 3.12

TFQ source compatibility

• update noisy expectation ops to avoid copy-constructing tensorflow::mutex, which fails with TensorFlow 2.19
• keep the rest of the TensorFlow Quantum codepath unchanged as much as possible

Docs / install flow

• document tf-keras==2.19.0 and TF_USE_LEGACY_KERAS=1 as part of the supported install path

Testing

Linux/Ubuntu 22.04 (Docker), Python 3.10, 3.11, and 3.12, Bazelisk → Bazel 6.5.0

• bazel build ... release:build_pip_package succeeds
• unit tests (./scripts/test_all.sh and ./scripts/ci_validate_tutorials.sh) pass
• wheel builds, installs, and imports successfully
• validated with TensorFlow 2.19.1 + tf-keras 2.19.0, Cirq 1.5.0, and NumPy 2.1.3

Notes

• This keeps TFQ on the Keras 2 path; Keras 3 migration can be handled in a future PR
• TensorFlow 2.19.1 keeps the same Eigen pin already used by TFQ
• this change should help with the incoming upgrades to 2.20
• Updating Bazel to 7.4.1 might be useful when upgrading to 2.20

[gemini-code-assist]

Code Review

This pull request updates TensorFlow Quantum to support TensorFlow 2.19.1 and TF-Keras 2.19.0, adjusting build configurations, documentation, and dependency management accordingly. Key changes include the enforcement of Python 3.10 for requirement generation and a migration from tensorflow::mutex to absl::Mutex in C++ core operations. Review feedback points out missing absl/synchronization/mutex.h headers and identifies a technical issue where absl::Mutex is stored in a std::vector, which is invalid due to its non-copyable and non-movable nature; a transition to std::unique_ptr is recommended.

[mhucka]

Looks good.

[mhucka]

/gemini review

[gemini-code-assist]

Code Review

This pull request updates the project to support TensorFlow 2.19.1 and TF-Keras 2.19.0. Key changes include updating dependency versions across README.md, WORKSPACE, setup.py, and requirement files, as well as updating installation documentation to include the TF_USE_LEGACY_KERAS=1 environment variable. In the C++ source, tensorflow::mutex has been replaced with absl::Mutex across multiple operation kernels. Furthermore, the generate_requirements.sh script now enforces Python 3.10 and utilizes updated pip-compile flags including hash generation. I have no feedback to provide as there were no review comments.