Hybrid Quantum-Classical Neural Network Demonstrates High Accuracy in Recognizing Quantum Phases of Matter

Identifying quantum phases of matter is key to understanding strongly correlated materials, but remains a challenging task for both conventional computers and current quantum processors.

Here, we introduce and implement a hybrid quantum-classical neural network for quantum phase recognition by combining a hardware-efficient parameterized quantum circuit and a feedforward neural network.

The classifier reaches accuracies above 85% in single-shot measurements, and above 99% when averaging over ten measurements.

We expect hybrid neural networks such as the one presented here to be a promising approach for characterizing quantum states in scenarios where classical methods exhibit an unfavorable scaling of sample complexity.