Active Learning with Variational Quantum Circuits for Quantum Process Tomography
This work addresses the challenge of fully characterizing quantum systems more efficiently, which is incremental but important for advancing quantum computing and information processing.
The authors tackled the problem of inefficient quantum state selection in quantum process tomography by proposing an active learning framework that adaptively chooses the most informative states, achieving significantly improved reconstruction with gains increasing for larger quantum systems up to seven qubits.
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.