Posted by Michael Broughton, Alan Ho, Masoud Mohseni
Last year we announced TensorFlow Quantum (TFQ) at the 2020 TensorFlow developer summit and on the Google AI Blog. 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 TensorFlow Quantum, we’ve been happy to see increasing use of the library in the academic world as well as inside Alphabet, in particular the Quantum AI team at Google. There have been many research articles published in the last year that made use of TensorFlow Quantum in quantum machine learning or hybrid quantum-classical models, including discriminative models and generative models. With the cross pollination of ideas between the two fields, we are also seeing advanced learning algorithms from classical machine learning being reimagined such as quantum reinforcement learning, layerwise, and neural architecture search. 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:
- Power of data in quantum machine learning (Google, Discrimitivate, 30 qubits)
- Absence of Barren Plateaus in Quantum Convolutional Neural Networks (LANL, Discriminative, 26 qubits)
- Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm (Alphabet X, Generative, 16 qubits)
- Entanglement Diagnostics for Efficient Quantum Computation (Princeton / Tel-Aviv U., Generative, 20 qubits)
- Layerwise learning for quantum neural networks (VW / Google, Discriminative, 18 qubits)
- Differentiable Quantum Architecture Search (Tencent, Neural Architecture Search, 8 qubits)
In our recent publication to quantify the computational advantage of quantum machine learning, experiments were conducted at PetaFLOP/s throughput scales, which is nothing new for classical machine learning, but represents a huge leap forward in the scale seen in quantum machine learning experiments before TensorFlow Quantum came along. We are very excited for the future that quantum computing and machine learning have together and we are happy to see TensorFlow Quantum having such a positive impact already.
The academic world isn’t the only place machine learning and quantum computing have been able to come together. Over the past year members of the TensorFlow Quantum team helped out in supporting the artistic works of Refik Anadol Studios’ “Quantum memories” piece. This combines the random circuit sample data from the 2019 beyond classical experiment and adoptions of StyleGAN to create some truly magnificent works of art
Quantum memories installation at the NGV (image used with permission).
We will soon be releasing TensorFlow Quantum 0.5.0, with more support for distributed workloads as well as lots of new quantum centric features and some small performance boosts. Looking forward, we hope that these features will enable our users to continue to push the boundaries of complexity and scale in quantum computing and machine learning and eventually help lead to groundbreaking quantum computing experiments (not just simulations). Our ultimate goal when we released TensorFlow Quantum was to have it aid in the search for quantum advantage in the field of machine learning. 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.