Add cuQuantum-based TFQ native ops and layers

[QuantumJaeYoo]

This has large changes and requires some deps error, rewrite documentations, and fixed internal bugs inside random ops, and so forth.

Please look at the PR descriptions in my repo to understand the procedures : https://github.com/jaeyoo/quantum/pulls?q=is%3Apr+is%3Aclosed

Breaking Changes

• Build, compilation, and packaging:
  • The TensorFlow dependency has been upgraded from 2.7.0 to 2.11.0:
  • TensorFlow Quantum is now compiled with _GLIBCXX_USE_CXX11_ABI=1. Downstream projects that encounter std::__cxx11 or [abi:cxx11] linker errors will need to adopt this compiler option. See the GNU C++ Library docs on Dual ABI.
  • TensorFlow Quantum is now compiled with -std=c++17, see install.md for build instructions.
• Cirq dependency has been upgraded from 0.13.1 to ~=1.0
  • cirq_google.XMON was deprecated : https://github.com/quantumlib/Cirq/issues/4856
  • QuantumEngineSampler was deprecated : https://github.com/quantumlib/Cirq/issues/5371
  • So, we need ProcessorSampler() for testing
  • cirq.CNOT interface was changed.
  • https://quantumai.google/reference/python/cirq/CNOT
  • No more control, target argument.
  • cirq.SingleQubitGate was deprecated.
  • For testing, use cirq.testing.SingleQubitGate : https://github.com/quantumlib/Cirq/pull/5272/files
  • For implementation, use cirq.Gate.

Major Features and Improvements

• Significant performance improvements by introducing cuQuantum support for circuit execution on Nvidia GPUs:
  • TensorFlow Quantum Keras layers can now be executed on GPU by setting the optional arguement use_cuquantum=True at layer instantiation. Examples:
  • tfq.layers.Expectation(use_cuquantum=True)
  • tfq.layers.SampledExpectation(use_cuquantum=True) (note that cuQuantum runtime is unsupported for any noisy circuit operations
  • tfq.layers.State(use_cuquantum=True)
  • tfq.layers.Sample(use_cuquantum=True)
  • tfq.layers.PQC(model_circuit, operators, use_cuquantum=True)
  • tfq.layers.ControlledPQC(model_circuit, operators, use_cuquantum=True)
  • Important notes:
    • CuQuantum execution is currently only supported for source distributions meaning that the user must build TensorFlow Quantum & tensorFlow-cpu from source following the instructions in install.md.
    • Ensure that the first entry is "N" in the configure.sh script at this step of building. This ensures that you build upon tensorflow-cpu as tensorflow-gpu is unnecessary for CuQuantum support in TensorFlow Quantum.
    • The cuQuantum SDK must be installed locally. See installation instructions for details. As part of the installation process, ensure that the CUQUANTUM_ROOT environment variable is set (referred to in the installation instructions). If not set, bazel will attempt to automatically locate the folder containing the cuQuantum installation upon running configure.sh at this step.
    • Tested on Titan, Ampere and Volta Nvidia GPU architectures. Note that Pascal GPU architectures are not supported, see documentation to check whether your GPU is compatible with cuQuantum
    • Quantum concurrency (global context option) should be turned off when use_cuquantum=True. This can be done by running: tfq.python.quantum_context.set_quantum_concurrent_op_mode(False)

Source: https://github.com/Sinestro38/quantum/blob/master/tensorflow_quantum/release.md cc: @MichaelBroughton @QuantumJaeYoo

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[QuantumJaeYoo]

@MichaelBroughton Hi Michael, @Sinestro38 and I could reduce the number of files in this PR for you to review this PR easily. PTAL.

[zaqqwerty]

Just a note, could it make sense to squash-merge instead of merge? Noticed commit history getting cluttered on main

[QuantumJaeYoo]

@zaqqwerty Thank you for the idea. I squashed all commits. Could you please also review this PR? @MichaelBroughton Please feel free to add kokoro:run tag and then let's debug it together.

[QuantumJaeYoo]

@zaqqwerty @MichaelBroughton After fixing the merge conflict, the squashed commits reverted... I hope we could use "Squash and merge" button before merging this PR.

[QuantumJaeYoo]

I will split this PR into multiple easier and shorter PRs.