TensorFlow Quantum (TFQ) is a Python framework for quantum machine learning. As an application framework, TFQ allows quantum algorithm researchers and ML application researchers to leverage Google’s quantum computing frameworks, all from within TensorFlow.
TensorFlow Quantum focuses on quantum data and building hybrid quantum-classical models. It provides tools to interleave quantum algorithms and logic designed in Cirq with TensorFlow. A basic understanding of quantum computing is required to effectively use TensorFlow Quantum.
To get started with TensorFlow Quantum, see the install guide and read through some of the runnable notebook tutorials.
TensorFlow Quantum implements the components needed to integrate TensorFlow with quantum computing hardware. To that end, TensorFlow Quantum introduces two datatype primitives:
- 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.
- 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.
Read more about the TensorFlow Quantum implementation in the design guide.
Report bugs or feature requests using the TensorFlow Quantum issue tracker.