Nonparametric Learning Non-Gaussian Quantum States of Continuous Variable Systems

TL;DR

This paper introduces a non-parametric kernel quantum state estimation framework that accurately reconstructs non-Gaussian quantum states from noisy data, enhancing quantum state characterization without prior assumptions.

Contribution

It presents a robust, non-parametric estimation method for quantum states using tomographic data, with near-optimal convergence and applicability to complex non-Gaussian states.

Findings

Achieves near-optimal convergence rate of O(T^{-1})

Robustly estimates states and trace quantities from noisy data

Effective for multimodal, non-Gaussian quantum states

Abstract

Continuous-variable quantum systems are foundational to quantum computation, communication, and sensing. While traditional representations using wave functions or density matrices are often impractical, the tomographic picture of quantum mechanics provides an accessible alternative by associating quantum states with classical probability distribution functions called tomograms. Despite its advantages, including compatibility with classical statistical methods, tomographic method remain underutilized due to a lack of robust estimation techniques. This work addresses this gap by introducing a non-parametric \emph{kernel quantum state estimation} (KQSE) framework for reconstructing quantum states and their trace characteristics from noisy data, without prior knowledge of the state. In contrast to existing methods, KQSE yields estimates of the density matrix in various bases, as well as trace quantities such as purity, higher moments, overlap, and trace distance, with a near-optimal convergence rate of $\tilde{O}\bigl(T^{-1}\bigr)$, where $T$ is the total number of measurements. KQSE is robust for multimodal, non-Gaussian states, making it particularly well suited for characterizing states essential for quantum science.