Entanglement-enhanced randomized measurement in noisy quantum devices

Abstract

Quantum hardware is advancing rapidly across various platforms, yet implementing large-scale quantum error correction (QEC) remains challenging. As hardware continues to improve, there is a growing need to identify potential applications on noisy quantum devices that can leverage these enhancements. With this motivation, we explore the advantages of shallow measurements over (non-entangling) single-qubit measurements for learning various properties of a quantum state. While previous studies have examined this subject, they have primarily focused on specific problems. Here, by developing a new theoretical framework, we demonstrate how shallow measurements can benefit in diverse scenarios. Despite the additional errors from two-qubit gates in shallow measurements, we experimentally validated improvements compared to single-qubit measurements in applications like derandomization, common randomized measurements, and machine learning up to 40 qubits and 46 layers of two-qubit gates, respectively. As a result, we show that hardware improvements, even before QEC, could broaden the range of feasible applications.