Quantum computing has made great strides in recent years, though it still faces significant challenges. If and when it gets here, machine learning may be ready for it.

What’s new: TensorFlow Quantum is a platform for building, training, and deploying neural networks on quantum processors. It was developed by Alphabet, Volkswagen, and the University of Waterloo.

How it works: The software works with quantum hardware like Google’s Sycamore computer, which has 54 qubits. Each qubit processes multiple calculations at once, theoretically enabling such systems to vastly outperform conventional CPUs.

  • TFQ marries TensorFlow with Cirq, a library that makes it easier to map machine learning algorithms to quantum circuitry. Researchers can use Cirq to prototype quantum neural networks built with layers of quantum circuits, and then embed their models within a TensorFlow graph.
  • The framework introduces two new concepts: quantum tensors and quantum layers. Quantum tensors are like normal tensors, but they store quantum superpositions (like storing many tensors at once, or storing a batch of tensors). Quantum layers operate on quantum tensors.
  • Not all operations are supported, but those that are get a quantum speedup. You can mix quantum tensors and quantum layers with normal tensors and layers, but the conversions between quantum tensors and regular tensors are slow.
  • Like the usual TensorFlow, the quantum version works with CPUs, GPUs, and TPUs, while adding QPUs to the mix.

Why it matters: Imagine that, instead of living one life, you could live billions of lives simultaneously, and at the end, you would have learned from all of them. Quantum speedups can be enormous for operations in which a single quantum computer can outperform the fastest supercomputer (that is, millions of classical computers working together). Machine learning could be one of those operations.

Yes, but: It may be a while before quantum computing is practical outside of research labs. Among other challenges, quantum systems are so sensitive that the noise they generate can derail their calculations.

Behind the news: Last year, Google claimed that Sycamore had achieved so-called quantum supremacy by performing a calculation that it deemed impractical for a classical supercomputer. IBM challenged the claim by solving the problem using conventional technology. The two tech giants, which are vying for leadership in the field, remain at loggerheads.

We’re thinking: Tech giants are always on the lookout for disruptions that may threaten their business. By creating tools for developers, they’re positioning themselves for a quantum future whether or not it arrives. Meanwhile, machine learning engineers have a shiny new toy to play with!

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