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Google’s TensorFlow Quantum Is a Key Industry Milestone in Machine Learning Acceleration
by James Kobielus | March 12, 2020

The News: Google’s TensorFlow Quantum is a key industry milestone in machine learning acceleration. Google announced the launch of TensorFlow Quantum earlier this week, a new software-only stack that extends the widely adopted TensorFlow open-source machine learning (ML) library and modeling framework to support building and training of ML models to be processed on quantum computing platforms. Read more about the Google announcement from VentureBeat.

Google’s TensorFlow Quantum Is a Key industry Milestone in Machine Learning Acceleration

Analyst Take: Google’s TensorFlow Quantum launch is exciting, bringing together both quantum and machine learning initiatives at the company. Morever, Google has achieved an artificial intelligence (AI) industry milestone with this release. Though early quantum-computing developers had used the approach to accelerate machine learning (ML) algorithms, none has until now had the benefit of a quantum framework and library that’s integrated with a popular ML open-source tool.

Bringing Quantum Computing Into the Dominant ML Modeling Framework

Google’s TensorFlow Quantum brings quantum computing into the dominant modeling framework used by today’s developers of ML, deep learning (DL), and other AI applications. Developed by Google’s X R&D unit, TensorFlow Quantum enables data scientists to use Python code to develop quantum ML models through standard Keras functions. It provides a library of quantum circuit simulators and quantum computing primitives that are compatible with existing TensorFlow APIs.

Recognizing that quantum computing is not yet mature enough to process the full range of ML workloads with sufficient accuracy, Google has wisely designed its new open-source tool to support the many AI use cases with one foot in traditional computing architectures. TensorFlow Quantum enables developers to rapidly prototype ML models that hybridize the execution of quantum and classic processors in parallel on learning tasks. Using the tool, developers can build both classical and quantum datasets, with the classical data natively processed by TensorFlow and the quantum extensions processing quantum data, which consists of both quantum circuits and quantum operators.

Developers can use TensorFlow Quantum for supervised learning on such ML use cases as quantum classification, quantum control, and quantum approximate optimization. They can also execute advanced quantum learning tasks such as meta-learning, Hamiltonian learning, and sampling thermal states.

In addition, Google has designed TensorFlow Quantum to support the growing range of AI use cases—such as “deepfakes”—that do video, voice, and image generation with a high degree of verisimilitude. Google ML developers can use TensorFlow Quantum to train hybrid quantum/classical models to handle both the discriminative and generative workloads at the heart of the generative adversarial networks used in such applications.

Google designed TensorFlow Quantum to support advanced research into alternative quantum computing architectures and algorithms for processing ML models. This makes the new offering an invaluable research tool for computer scientists who are experimenting with different quantum and hybrid processing architectures optimized for ML workloads.

To this end, TensorFlow Quantum incorporates Cirq, an open-source Python library for programming quantum computes. It supports programmatic creation, editing, and invoking of the quantum gates that constitute the Noisy Intermediate Scale Quantum (NISQ) circuits characteristic of today’s quantum systems.

Cirq enables developer-specified quantum computations to be executed in simulations or on real hardware. It does this by converting quantum computations to tensors for use inside TensorFlow computational graphs. As an integral component of TensorFlow Quantum, Cirq enables quantum circuit simulation and batched circuit execution, as well as estimation of automated expectation and quantum gradients. It also enables developers to build efficient compilers, schedulers, and other algorithms for NISQ machines. Here’s an interesting video that walks you through programming a quantum computer with Cirq if you’d like a deeper dive:

Google’s Open-Source Tool for Quantum ML Demonstrates its Market-Leading AI R&D Prowess

Google’s R&D labs seem to be producing an endless stream of innovative research into both AI/ML and into quantum computing.

TensorFlow Quantum’s release is no big surprise, coming several months after Google declared “quantum supremacy”, which refers to its achievement of a quantum computing feat that would have been impossible on traditional computing architecture. For the past several years, researchers at various commercial and research institutions have been demonstrating that quantum processing of ML models is in fact feasible. However, one can expect that AI/ML researchers at Google and elsewhere will probably use TensorFlow Quantum to do some fairly amazing things that were never feasible on traditional AI-accelerator hardware platforms.

In addition to providing a full AI/ML software stack into which quantum processing can now be hybridized, Google is looking to expand the range of more traditional chip architectures on which TensorFlow Quantum can simulate quantum ML. Google has also announced plans to expand the range of custom quantum-simulation hardware platforms supported by the tool to include graphics processing units from various vendors as well as its own Tensor Processing Unit AI-accelerator hardware platforms.

Google’s New Tool Will Spur AI/ML Projects Throughout the Quantum Industry

Google’s latest announcement lands in a fast-moving, but still immature, quantum computing marketplace. By extending the most popular open-source ML development framework, Google will almost certainly catalyze use of TensorFlow Quantum in a wide range of ML-related initiatives.

Recent moves by Amazon Web Services, Microsoft, IBM, and Honeywell in the quantum computing space address ML use cases to varying degrees:

  • Announced in November 2019, Azure Quantum is a quantum-computing cloud service that is currently in private preview and expected to become generally available later this year. It comes with a Microsoft open-sourced Quantum Development Kit for the Microsoft-developed quantum-oriented Q# language as well as Python, C#, and other languages. The kit includes libraries for development of quantum apps in ML, cryptography, optimization, and other domains. Microsoft’s plan is to integrate this kit and such development tools and Visual Studio so they can be used to build quantum programs for quantum hardware platforms from Honeywell, IonQ, QCI, and others, and also to simulate program performance on these and other platforms.
  • Announced in December 2019, the Amazon Braket service, still in preview, is a fully managed AWS service that enables scientists, researchers, and developers to begin experimenting with computers from quantum hardware providers (including D-Wave, IonQ, and Rigetti) in a single place. It allows users to explore, evaluate, and experiment with quantum computing hardware to gain in-house experience as they plan for the future. It provides a single development environment to build quantum algorithms—including ML–and test them on simulated quantum computers. It enables developers to run ML and other quantum programs on a range of different hardware architectures. It allows users to design quantum algorithms using the Amazon Braket developer toolkit and use familiar tools such as Jupyter notebooks. This gives customers the choice of executing either low-level quantum circuits or fully managed hybrid algorithms, and select from a range of software simulators and their choice of quantum hardware.
  • In January 2020, IBM announced the expansion of its Q Network, under which over 200,000 users are running hundreds of billions of executions on IBM’s quantum systems and simulators through the IBM Cloud. Participants in the network have access to IBM’s quantum expertise and resources, open source Qiskit software and developer tools, as well as cloud-based access to the IBM Quantum Computation Center. Many of the workloads being run include ML, as well as real-time simulations of quantum computing architectures.
  • In March 2020, Honeywell announced that its quantum computer will be generally available within three months, with a quantum volume of at least 64. Fellow analyst here at Futurum, Daniel Newman, covered this in detail here: Honeywell Drives Quantum Forward With New Roadmap and Partnerships. Honeywell also announced that Honeywell Ventures is making investments in Cambridge Quantum Computing and Zapata Computing, both of whom have expertise in ML and other cross-vertical algorithms and software for quantum computing applications.

The Takeaway—Google’s Open-Source Tool Will Make ML Quantum’s Killer App

Quantum computing has been in wait-and-see mode for so long that we tend to overlook the fact that it’s being rapidly put to practical uses.

If quantum computing were to take 60 years to mature into a commercial reality—in other words, roughly the amount of time it took for ML to emerge from laboratory curiosity to practical tool—we wouldn’t see quantum computing until the year 2075. But it’s clear, from all the recent industry announcements discussed above, that we’ll not only see commercialized quantum computing in our lifetimes, but that it’s already begun to emerge and will gain steady adoption in this decade, even while research labs roll out ever more sophisticated quantum architectures.

Also, it’s become clear that ML, deep learning, and other AI workloads will be quantum’s killer application. The new Google-developed quantum ML framework will find its way into a wide range of other solution providers’ quantum computing environments. As it does, AI developers will be able to build and train ML models that can achieve heretofore unimaginably intelligent algorithmic feats.

On final takeaway concerns what Google’s likely next move will be in the quantum ML space. My prediction is that before 2020 comes to a close, Google will combine TensorFlow Quantum with its pre-existing Quantum Computing Playground into a full-featured, managed quantum ML service.

Considering the fact that Google’s top public-cloud rivals–Microsoft, AWS, and IBM—all have such services either on the market or in preview, it would be shocking if the Mountain View, California-based company doesn’t try to one-up them with a quantum-ML service of its own.

Futurum Research provides industry research and analysis. These columns are for educational purposes only and should not be considered in any way investment advice.

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Image Credit: The Next Web
James Kobielus
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