Universal quantum state preparation via revised greedy algorithm

TL;DR

This paper introduces a revised greedy algorithm for universal quantum state preparation, enabling efficient and robust preparation of arbitrary quantum states without training, outperforming existing methods in quality and noise resilience.

Contribution

A novel revised greedy algorithm for quantum state preparation that improves global optimization and robustness, applicable to various quantum systems.

Findings

Outperforms alternative numerical optimization methods in quality

Generates pulse sequences robust against errors and noise

Does not require training unlike machine learning approaches

Abstract

Preparation of quantum state lies at the heart of quantum information processing. The greedy algorithm provides a potential method to effectively prepare quantum states. However, the standard greedy algorithm, in general, cannot take the global maxima and instead becomes stuck on a local maxima. Based on the standard greedy algorithm, in this paper we propose a revised version to design dynamic pulses to realize universal quantum state preparation, i.e., preparing any arbitrary state from another arbitrary one. As applications, we implement this scheme to the universal preparation of single- and two-qubit state in the context of semiconductor quantum dots and superconducting circuits. Evaluation results show that our scheme outperforms the alternative numerical optimizations with higher preparation quality while possesses the comparable high efficiency. Compared with the emerging machine learning, it shows a better accessibility and does not require any training. Moreover, the numerical results show that the pulse sequences generated by our scheme are robust against various errors and noises. Our scheme opens a new avenue of optimization in few-level system and limited action space quantum control problems.