Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning

Posted by Alan Ho, Product Lead and Masoud Mohseni, Technical Lead, Google Research

Cross posted with the Google AI
, , and , the development of new quantum ML models could have a , , we announce the release of (NISQ) processors with ~50 - 100 qubits.

Under the hood, TFQ integrates , 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.

What is a Quantum ML Model?

A quantum model has the ability to represent and generalize data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced - quantum data and hybrid quantum-classical models.

Quantum data exhibits , leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. Quantum data, which can be generated / simulated on quantum processors / sensors / networks include the , , ; each example can be run in-browser via Colab from our . Training can be done using standard Keras functions.

To provide some intuition on how to use quantum data, one may consider an supervised classification of quantum states using a quantum neural network. Just like classical ML, a key challenge of quantum ML is to classify “noisy data”. To build and train such a model, the researcher can do the following:

1. Prepare a quantum dataset - Quantum data is loaded as tensors (a multi-dimensional array of numbers). Each quantum data tensor is specified as a quantum circuit written in example, the .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. To achieve the latter, we are also announcing the release of qsim (, which has demonstrated the ability to simulate a 32 qubit quantum circuit with a gate depth of 14 in 111 seconds on a single Google Cloud node for details). The simulator is particularly optimized for multi-core Intel processors. Combined with TFQ, we have demonstrated 1 million circuit simulations for 20 qubit quantum circuit at a gate depth of 20 in 60 minutes on a Google Cloud node (, Section II E on the Quantum Circuit Simulation with qsim for more information.
   

   Looking Forward

   Today, TensorFlow Quantum is primarily geared towards executing quantum circuits on classical quantum circuit simulators. In the future, TFQ will be able to execute quantum circuits on actual quantum processors that are supported by Cirq, including Google’s own processor and visit the