近日，中国科学技术大学解围团队研究了基于变分量子电路的量子过程断层扫描主动学习。相关论文发表在2025年10月16日出版的《物理评论A》杂志上。

量子过程层析成像（QPT）是全面表征量子系统的基本工具。它依赖于查询一组量子态作为量子过程的输入。之前的QPT方法通常采用一种直接的策略来随机选择量子态，忽略了它们之间信息的差异。

在这项工作中，研究组提出了一个通用的主动学习（AL）框架，该框架自适应地选择最具信息量的量子态子集进行重建。研究组设计和评估了各种人工智能算法，并提供了在不同场景下选择合适方法的实用指南。特别是，他们引入了一个学习框架，该框架利用广泛应用的变分量子电路来执行QPT任务，并将其人工智能算法集成到查询步骤中。

研究组通过重建由多达七个量子比特的随机量子电路产生的幺正量子过程来证明该算法。数值结果表明，他们的人工智能算法实现了显著的改进重建，并且改进程度随着底层量子系统的大小而增加。该工作为进一步推进现有的QPT方法开辟了新的途径。

附：英文原文

Title: Active learning with variational quantum circuits for quantum process tomography

Author: Jiaqi Yang, Xiaohua Xu, Wei Xie

Issue&Volume: 2025/10/16

Abstract: Quantum process tomography (QPT) is a fundamental tool for fully characterizing quantum systems. It relies on querying a set of quantum states as input to the quantum process. Previous QPT methods typically employ a straightforward strategy for randomly selecting quantum states, overlooking differences in informativeness among them. In this work we propose a general active learning (AL) framework that adaptively selects the most informative subset of quantum states for reconstruction. We design and evaluate various AL algorithms and provide practical guidelines for selecting suitable methods in different scenarios. In particular, we introduce a learning framework that leverages the widely used variational quantum circuits to perform the QPT task and integrate our AL algorithms into the query step. We demonstrate our algorithms by reconstructing the unitary quantum processes resulting from random quantum circuits with up to seven qubits. Numerical results show that our AL algorithms achieve significantly improved reconstruction and the improvement increases with the size of the underlying quantum system. Our work opens new avenues for further advancing existing QPT methods.

DOI: 10.1103/wr3n-v85k

Source: https://journals.aps.org/pra/abstract/10.1103/wr3n-v85k