Wisdom of Crowds in Quantum Machine Learning

Abstract

A collective outcome or decision from a crowd often prevails over that of a single expert. Here we study this phenomenon through the lens of quantum machine learning. We compare the performance of an expert, a highly trained quantum network, to a crowd, several poorly trained ones, for quantum information processing tasks such as quantum tomography and entanglement recognition. We also show quantum-enhanced performance from a temporal crowd by using time multiplexing on a single poorly trained quantum network. Given the same resources for training, we show that the crowd outperforms the expert by a definitive margin.