TensorFlow Quantum

TensorFlow Quantum focuses on quantum data and building hybrid quantum-classical models. It integrates quantum computing algorithms and logic designed in Cirq, and provides quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators.

TensorFlow Quantum implements the components needed to integrate TensorFlow with quantum computing hardware. To that end, TensorFlow Quantum introduces two datatype primitives:

1. Quantum circuit —This represents a Cirq-defined quantum circuit within TensorFlow. Create batches of circuits of varying size, similar to batches of different real-valued datapoints.
2. Pauli sum —Represent linear combinations of tensor products of Pauli operators defined in Cirq. Like circuits, create batches of operators of varying size.

Using these primitives to represent quantum circuits, TensorFlow Quantum provides the following operations:

• Sample from output distributions of batches of circuits.
• Calculate the expectation value of batches of Pauli sums on batches of circuits. TFQ implements backpropagation-compatible gradient calculation.
• Simulate batches of circuits and states. While inspecting all quantum state amplitudes directly throughout a quantum circuit is inefficient at scale in the real world, state simulation can help researchers understand how a quantum circuit maps states to a near exact level of precision.