End-to-End Neural and Quantum Transcoding for Compressed Latent Representation under Channel Noise

TL;DR

This paper introduces a novel end-to-end learnable quantum transcoding scheme that combines neural data compression with quantum encoding, optimizing for robustness and efficiency in noisy quantum communication.

Contribution

It presents a new integrated neural and quantum encoding method that improves data compression and robustness without requiring full quantum state reconstruction.

Findings

Achieves high reconstruction accuracy under extreme noise conditions.

Enables efficient quantum tomography through normalized quantum observables.

Outperforms traditional encoding schemes in robustness and compactness.

Abstract

Recent advancements in quantum computing highlight the need for efficient encoding of classical data into quantum states to ensure robust quantum information processing. Traditional encoding schemes often impose impractical requirements about the knowledge of quantum states and lack adaptability to noisy quantum channels and broader tasks. To address these limitations, we propose a novel end-to-end learnable quantum transcoding scheme explicitly optimized for compactness and robustness in noisy quantum communication scenarios. Our approach integrates neural network-based data compression with Cholesky decomposition-based quantum encoding and bypasses full density matrix reconstruction. Through normalized quantum observables, our method enables efficient tomography and achieves high reconstruction and classification performance even under extreme noise conditions.