Active Learning with Variational Quantum Circuits for Quantum Process Tomography

TL;DR

This paper introduces an active learning framework using variational quantum circuits to improve quantum process tomography, significantly enhancing reconstruction accuracy especially for larger quantum systems.

Contribution

It presents a novel active learning approach integrated with variational quantum circuits for more efficient quantum process tomography, outperforming traditional random sampling methods.

Findings

Active learning algorithms improve quantum process reconstruction accuracy.

The approach scales better with increasing qubit numbers.

Numerical results show significant improvements for systems up to seven qubits.

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 (VQCs) 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.