On March 9, the Google AI team published an official blog officially releasing TensorFlow Quantum (TFQ), an open-source library for rapidly building prototypes of quantum machine learning models. TFQ provides the necessary tools to bring the quantum computing and machine learning research communities together to control and model natural or artificial quantum systems, such as Noisy Intermediate Scale Quantum (NISQ) processors with about 50-100 qubits.
A key feature of TensorFlow Quantum is the ability to train and execute many quantum circuits simultaneously. TensorFlow can parallelize computation across computer clusters and simulate relatively large quantum circuits on multi-core computers, achieving this. To achieve the latter, we also announced the release of a new high-performance open-source quantum circuit simulator, qsim, which has proven it can simulate a 32-qubit quantum circuit with gate depth 14 in 111 seconds on one Google Cloud node (n1-ultramem-160). The simulator is specifically optimized for multi-core Intel processors. Combined with TFQ, we have performed 1 million circuit simulations of a 20-qubit quantum circuit at 20 gate depth in 60 minutes on a Google Cloud node (n2-highcpu-80). For more information, see Section II E of the TFQ white paper, “Quantum Circuit Simulation with qsim”.