Open-source library for rapid prototyping of quantum ML models

March 12, 2020 //By Rich Pell
Open-source library for rapid prototyping of quantum ML models
Tech giant Google has announced the launch of an open source library for quantum machine learning (ML).

TensorFlow Quantum (TFQ) is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models. With the recent progress in the development of quantum computing, says the company, the development of new quantum ML models could have a profound impact on the world’s biggest problems, leading to breakthroughs in the areas of medicine, materials, sensing, and communications.

TFQ is offered as providing the tools necessary for bringing the quantum computing and machine learning research communities together to control and model natural or artificial quantum systems, for example, noisy intermediate scale quantum (NISQ) processors with ~50 - 100 qubits. TFQ integrates the Cirq open source framework for NISQ algorithms with TensorFlow machine learning platform, and offers high-level abstractions for the design and implementation of both discriminative and generative quantum-classical models by providing quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators.

TFQ allows researchers to construct quantum datasets, quantum models, and classical control parameters as tensors in a single computational graph. The outcome of quantum measurements, leading to classical probabilistic events, is obtained by TensorFlow Ops. Training can be done using standard Keras functions.

A key feature of TensorFlow Quantum is the ability to simultaneously train and execute many quantum circuits. This is achieved by TensorFlow’s ability to parallelize computation across a cluster of computers, and the ability to simulate relatively large quantum circuits on multi-core computers.

While TensorFlow Quantum is primarily geared towards executing quantum circuits on classical quantum circuit simulators, says the company, on the future it will be able to execute quantum circuits on actual quantum processors that are supported by Cirq, including Google’s own processor Sycamore. For more see the white paper " TensorFlow Quantum: A Software Framework for Quantum Machine Learning ."

Google AI

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