Experimental realization of a bidirectional quantum analog-to-digital converter between photonic wavefront and qubits

Quantum systems can be fundamentally classified into discrete-variable (DV) and continuous-variable (CV) systems. The degree of quantum advantage achieved with a quantum system is often dictated by the scalability, robustness, and operational feasibility-factors that are closely tied to the nature of the variables employed. Notably, DV and CV systems are largely complementary in these factors. Consequently, integrating DV and CV variables holds significant promise for overcoming the inherent challenges associated with relying solely on one type of variable system. In this study, we build a bidirectional quantum analog-to-digital converter (QADC/QDAC), which facilitates deterministic and reversible quantum information transfer between CV and DV systems. This QADC/QDAC is hardware-efficient, and the error decreases exponentially with the number of qubits, thereby allowing high-fidelity information transfer with a few qubits. With this photonic QADC/QDAC, we demonstrate efficient CV mode tomography leveraging DV tomography techniques and achieve deterministic generation of non-Gaussian CV mode from qubits. Our findings address a critical barrier in constructing hybrid quantum processors and open up new avenues for scientific exploration previously inaccessible to single-variable quantum systems. A quantum analog-to-digital converter is built between photonic wavefront and qubits, which is bidirectional, fully optical, efficient and deterministic-thereby unifying the complementary advantages of discrete-variable and continuous-variable platforms within one architecture.

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Publication Details

Journal
Nature Communications
Published
2026-07-27
DOI
https://doi.org/10.1038/s41467-026-75903-y
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Experimental realization of a bidirectional quantum analog-to-digital converter between photonic wavefront and qubits

Gong-Chu Li, Chuan‐Feng Li, Yuancheng Liu, Xu-Song Hong et al.
Nature Communications
Neural Networks and Reservoir Computing
article

Experimental realization of a bidirectional quantum analog-to-digital converter between photonic wavefront and qubits

Gong-Chu Li, Chuan‐Feng Li, Yuancheng Liu, Xu-Song Hong, Geng Chen, H. Xu, Si-Qi Zhang, Lei Chen, Guang-Can Guo
article en

Abstract

Quantum systems can be fundamentally classified into discrete-variable (DV) and continuous-variable (CV) systems. The degree of quantum advantage achieved with a quantum system is often dictated by the scalability, robustness, and operational feasibility-factors that are closely tied to the nature of the variables employed. Notably, DV and CV systems are largely complementary in these factors. Consequently, integrating DV and CV variables holds significant promise for overcoming the inherent challenges associated with relying solely on one type of variable system. In this study, we build a bidirectional quantum analog-to-digital converter (QADC/QDAC), which facilitates deterministic and reversible quantum information transfer between CV and DV systems. This QADC/QDAC is hardware-efficient, and the error decreases exponentially with the number of qubits, thereby allowing high-fidelity information transfer with a few qubits. With this photonic QADC/QDAC, we demonstrate efficient CV mode tomography leveraging DV tomography techniques and achieve deterministic generation of non-Gaussian CV mode from qubits. Our findings address a critical barrier in constructing hybrid quantum processors and open up new avenues for scientific exploration previously inaccessible to single-variable quantum systems. A quantum analog-to-digital converter is built between photonic wavefront and qubits, which is bidirectional, fully optical, efficient and deterministic-thereby unifying the complementary advantages of discrete-variable and continuous-variable platforms within one architecture.

Nature Communications
University of Science and Technology of China (CN), Anhui University (CN), Hefei University (CN), QuantumCTek (China) (CN)
National Natural Science Foundation of China, University of Science and Technology of China, National Science and Technology Major Project
Openalex Percentile: Top 7%
Neural Networks and Reservoir Computing
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