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TensorFlow Quantum 0.5.0
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@MichaelBroughton MichaelBroughton released this 17 May 00:20
· 157 commits to master since this release
v0.5.0
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TensorFlow Quantum 0.5.0 includes new features, bug fixes and minimal API changes.

New Features/Improvements:
Added support for Cirq gates that have arbitrary control via the gate.controlled_by function. (Gradient support as well)
Added tfq.math.inner_product gradient. This op will now provide a gradient via tf.GradientTape.
Added tfq.math.fidelity op and gradient. This op will now provide a gradient via tf.GradientTape.
Added support in tfq.convert_to_tensor for circuits containing any Cirq noise channel from common_channels .
Added tfq.noise.expectation op and support with existing Differentiators for noisy analytic expectation calculation. Noisy simulations done via monte-carlo/trajectory sampling.
Added tfq.noise.samples op to draw bitstring samples from noisy circuits.
Added tfq.noise.sampled_expectation op and support with existing Differentiators for sample based expectation calculation.
Introduced get_gradient_circuits interface method for differentiators for users wanting to define a custom Differentiator.
Updated tfq.layers.Expectation, tfq.layers.Samples, tfq.layers.SampledExpectation with __init__ parameter backend=noisy, backend='noiseless' to support noisy circuits.
Added tfq.layers.NoisyPQC and tfq.layers.NoisyControlledPQC which are noisy equivalents of tfq.layers.PQC and tfq.layers.ControlledPQC.
New datasets available via tfq.datasets.
Improved stability and performance in distributed training with MultiWorkerMirroredStrategy and ParameterServer.

Bug fixes
Fixed an issue where backward passes done on expectation ops with empty input tensors would cause SEGFAULT.
Fixed inconsistent output shapes between some ops, when input was the empty tensor.
Fixed randomness sources used for sampling to use thread safe philox_random approaches from TF instead of std::mt19937 from the standard library.
Removed parallel calls to custom Cirq simulators when using backend != None inside of any tfq.layers. This is to ensure compatibility with high performance remote simulators as well as when running on real devices.

Breaking changes
We now depend on cirq==0.11.0 and tensorflow==2.4.1.

A big thanks to all of our contributors for this release:
@zaqqwerty , @jaeyoo , @lamberta , @MarkDaoust , @MichaelBroughton , @therooler , @sjerbi, @balopat , @lockwo, @gatorwatt .

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