Posted by Michael Broughton, Alan Ho, Masoud Mohseni

Last year we announced TensorFlow Quantum (TFQ) at the . Bringing all of the tools and features that TensorFlow has to offer to the world of quantum computing has led to some great research success stories. In this post, we would like to look back on what’s happened in the last year involving TensorFlow Quantum and how far it’s come. We also discuss the future of quantum computing and machine learning in TensorFlow Quantum.

Since the release of and , . We leverage the scalability and tooling of TensorFlow to run numerical experiments with large numbers of qubits and gates to more faithfully discover algorithms that will be practical in the future.

Here are a few papers published using TFQ if you’d like to check them out:

• (LANL, Discriminative, 26 qubits)
• (Princeton / Tel-Aviv U., Generative, 20 qubits)
• (Tencent, Neural Architecture Search, 8 qubits)

piece. This combines the random circuit sample data from the 2019 beyond to create some truly magnificent works of art

Next steps

We will soon be releasing . In time, it is our hope to see the world reach that goal, with the help of the continued hard work and dedication of the QML research community. Quantum machine learning is still a very young field and there’s still a long way to go before this happens, but over the past year we’ve seen the community make amazing strides in many different areas and we can’t wait to see what you will accomplish in the years to come.