Enhanced natural parameterized quantum circuit

Abstract

The classical-quantum interface for loading classical data into quantum systems is an indispensable component of quantum information processing, and the parameterized quantum circuit (PQC) represents significant methodology in this area. However, a challenge in designing parameterized quantum circuits that can fully utilize limited qubit resources while accurately representing classical data remains. To address this issue, we propose the enhanced natural parameterized quantum circuit (ENPQC), which achieves the maximum parameter capacity of quantum systems and preserves the local structure of the original dataset. To make the encoding circuits more experiment friendly, we further provide two near-optimal circuits that achieve near-maximum capacity and are comparable in complexity to the existing PQCs. We numerically show that the ENPQC overwhelms other encoding methods for multiple datasets, demonstrating its potential for machine learning tasks. Furthermore, we experimentally validate our scheme by demonstrating a three-class classification task on an NMR platform, which achieves over 97% accuracy. This work provides a powerful tool for the classical-quantum interface, paving the way for quantum big data processing during the noisy intermediate-scale quantum era.