Efficient learning of mixed-state tomography for photonic quantum walk

TL;DR

This paper introduces a neural-network-based approach for efficient mixed-state tomography in photonic quantum walks, achieving high fidelity with fewer measurements and faster training, thus overcoming physical constraints in quantum state verification.

Contribution

The paper presents a novel neural density operator and a compact interferometric device for scalable, high-fidelity mixed-state reconstruction in photonic quantum walks, reducing measurement costs.

Findings

Achieved ~97.5% fidelity in mixed-state reconstruction

Reduced measurement requirements by 50%

Enabled scalable experimental learning of mixed states

Abstract

Noise-enhanced applications in open quantum walk (QW) have recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challenging, as mixed-state tomography is a resource-intensive process, and implementing all required measurements is almost impossible due to various physical constraints. To address this challenge, we present a neural-network-based method for reconstructing mixed states with a high fidelity (~97.5%) while costing only 50% of the number of measurements typically required for open discrete-time QW in one dimension. Our method uses a neural density operator that models the system and environment, followed by a generalized natural gradient descent procedure that significantly speeds up the training process. Moreover, we introduce a compact interferometric measurement device, improving the scalability of our photonic QW setup that enables experimental learning of mixed states. Our results demonstrate that highly expressive neural networks can serve as powerful alternatives to traditional state tomography.