Variational Quantum Transduction

TL;DR

This paper introduces a variational quantum transduction framework that optimizes quantum signal transfer protocols, surpassing existing schemes in non-adaptive settings and approaching optimality in adaptive scenarios.

Contribution

The paper presents a novel variational quantum transduction method that leverages near-term quantum computing tools to systematically enhance protocol performance.

Findings

VQT surpasses all known non-adaptive transduction schemes.

VQT marginally improves adaptive Gaussian transduction strategies.

Training VQT is efficient and avoids barren plateau issues.

Abstract

Quantum transducers are critical for quantum interconnect, enabling coherent signal transfer across disparate frequency domains. Beyond material and device advances, protocol design has become a powerful means to improve transduction. We introduce a variational quantum transduction (VQT) framework that employs variational tools from near-term quantum computing to systematically optimize protocol performance. As a variational quantum circuit framework, VQT is not plagued by known training issues such as barren plateau, because a small-scale problem is sufficient for substantial advantage and training only needs to be done once to configure a VQT system. Maximizing the quantum information rate within this framework yields protocols that surpass all known schemes in their respective classes. For non-adaptive protocols, VQT exceeds the performance envelopes of Gottesman-Kitaev-Preskill (GKP)-based and entanglement-assisted approaches. In the adaptive setting, VQT provides only a marginal improvement over Gaussian feedforward strategies, indicating that Gaussian adaptive transduction is already close to optimal. With increasingly universal quantum control, VQT provides a systematic path toward optimal quantum transduction.