Speaker
Description
With increasing maturity of quantum computers, standard methods for characterizing global properties of their output quantum states via direct measurements and classical post-processing are becoming increasingly impractical due to large measurement costs. Here, we introduce, numerically benchmark and experimentally realize a hybrid quantum-classical neural network, consisting of a shallow parameterized quantum circuit followed by measurements and a classical neural network. The parameterized quantum circuit performs a nonlocal transformation of the measurement basis, which is jointly trained with the classical neural network to maximize the statistical distance between data obtained by measuring different quantum states. Using supervised learning, we demonstrate that the hybrid neural network distinguishes the topological phase of the surface code from both a symmetry-enriched topological phase and random product states. Compared with a classical neural network trained on randomized Pauli measurements, the hybrid neural network reduces both training and inference sample complexities for recognizing topological phases by approximately one order of magnitude. Enabled by its shallow quantum circuit, we demonstrate training of the hybrid neural network on a superconducting quantum processor with quantum hardware in the loop. These results establish the hybrid neural network as a promising framework for sample-efficient characterization of complex quantum states on current and near-term quantum hardware.
| Topic | Hybrid quantum-classical approaches for learning and optimization |
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