Google Announces TensorFlow Quantum 0.5.0: Expected Features & Updates

Since its launch at the 2020 TensorFlow developer summit, TFQ has brought exciting tools and features for quantum computing research.


Google is celebrating the first anniversary of TensorFlow Quantum (TFQ), a library for rapid prototyping of hybrid quantum-classical ML models. TFQ is regarded as a tipping point for developments in hybrid quantum and classic machine learning models the company has been pushing for years.

TensorFlow Computing

Since its launch at the 2020 TensorFlow developer summit, TFQ has brought exciting tools and features for quantum computing research. TFQ was developed in collaboration with the University of Waterloo, X, and Volkswagen. It integrates Cirq with TensorFlow to offer high-level abstractions to design and implement both discriminative and generative quantum-classical models. TFQ provides researchers with quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators.


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TFQ consists of structures such as qubits, gates, circuits, and measurement operators required for specifying quantum computations. It allows users to conduct user-specific computations, which can be executed in simulation or on real hardware

Cirq contains substantial machinery to design efficient algorithms and run them on quantum circuit simulators and eventually on quantum processors.

So far, it has been used for hybrid quantum-classical convolutional neural networks, machine learning for quantum control, layer-wise learning for quantum neural networks, quantum dynamics learning, generative modelling of mixed quantum states, reinforcement learning, etc 

Google will soon release TensorFlow Quantum 0.5.0, with more support for distributed workloads, many new quantum centric features and performance boosts.

What To Expect From TensorFlow Quantum 0.5.0

In 2019, Google claimed it achieved Quantum Supremacy with its new 54-qubit processor, Sycamore. TFQ 0.5.0 is expected to accelerate the company’s quantum computing efforts further. 

Below are a few developments we can expect: 

Expanding the horizons of quantum research: Though quantum computing has seen a lot of progress in the last few years, research tools to develop useful quantum ML models that can process quantum data and execute on quantum computers available today are still lacking. While TFQ has provided researchers with these tools, the updated version may bring advanced capabilities that can help speed up research in medical sciences, weather sciences, and more.

Accelerating Google quantum research: Quantum computing research has been the focus area of Google to push the boundaries in quantum computing and machine learning. Some of the current focus areas include work around quantum chemistry, microwaves in quantum computing, and more. Google is leading groundbreaking efforts in practical quantum computing experiments and not just simulators.

Improvement of simulation benchmarks: TFQ 0.5.0 is expected to drastically improve the benchmarks of simulations vs Cirq, designed for quantum computing researchers interested in running and developing algorithms that leverage existing quantum computers. 

Speed up implementation: Not only is TFQ 0.5.0 expected to speed up quantum research, but it allows for easy implementation of ideas that would otherwise never get tested. Researchers believe implementation is a common hindrance to new and interesting ideas. Far too many projects get stuck at the idea stage due to difficulties in transitioning the concept to reality, something TFQ makes easy.

Execute quantum circuits on actual quantum processors: Today, TensorFlow Quantum is primarily geared towards executing quantum circuits on classical-quantum circuit simulators. With the future versions, TFQ might run quantum circuits on actual quantum processors supported by Cirq, including Google’s processor, Sycamore. With TFQ 0.5.0, researchers hope to expand the range of custom simulation hardware supporting GPU and TPU integration.

Achieve quantum advantage in machine learning: Researchers expect TFQ 0.5.0 would aid in the quest for quantum advantage in the field of machine learning.

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Srishti Deoras
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.

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