Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Research outreach

Photonic Inc: Creating relevant, scalable, and distributed quantum computing

Stephanie Simmons

Published Jan 1, 2024• 0 citations
Publisher
DOI
10.32907/ro-144-8084755565

Related papers

Distributed Quantum Computing for Scalable Systems

Daniel Casado-Faulí, Parfait Atchade-Adelomou, David Pérez de Lara +1 more

Lecture notes in networks and systems•Jan 1, 2026•0 citations

dqc_simulator: an easy-to-use distributed quantum computing simulator

Kenny Campbell

Distributed quantum computing (DQC) is a promising proposal for overcoming the scalability challenges of quantum computing. However, the evaluation of DQC hardware and software is difficult due to the relative dearth of classical simulation tools available for DQC devices. In this work, we introduce dqc_simulator, a novel simulation toolkit, written in Python, which automates many of the most challenging aspects of the DQC simulation workflow. dqc_simulator enables the easy simulation of both hardware and software, making it easy to create realistic and robust tests and benchmarks for the full DQC stack.

quant-pharXiv:2604.13909•Apr 16, 2026•0 citations

SDQC: Distributed Quantum Computing Architecture Utilizing Entangled Ion Qubit Shuttling

Seunghyun Baek, Seok-Hyung Lee, Dongmoon Min +1 more

We propose Shuttling-based Distributed Quantum Computing (SDQC), a hybrid architecture that combines the strengths of physical qubit shuttling and distributed quantum computing to enable scalable trapped-ion quantum computing. SDQC performs non-local quantum operations by distributing entangled ion qubits via deterministic shuttling, combining the high-fidelity and deterministic operations of shuttling-based architectures with the parallelism and pipelining advantages of distributed quantum computing. We present (1) a practical architecture incorporating quantum error correction (QEC), (2) pipelining strategies to exploit parallelism in entanglement distribution and measurement, and (3) a performance evaluation in terms of logical error rate and clock speed. For a 256-bit elliptic-curve discrete logarithm problem (ECDLP) instance, which requires 2,871 logical qubits at code distance 13, SDQC achieves a logical error rate which is $1.20^{+0.94}_{-0.45}\times10^{-8}$ of Photonic DQC error rate and $3.79^{+5.09}_{-2.84}\times10^{-3}$ of Quantum Charge-Coupled Device (QCCD) error rate, while providing 2.82 times faster logical clock speed than QCCD.

quant-pharXiv:2512.02890•Dec 3, 2025•0 citations

Scalable photonic quantum technologies

Hui Wang, Timothy C. Ralph, Jelmer J. Renema +2 more

Nature Materials•Aug 14, 2025•31 citations

Photonic quantum technologies

Jeremy L. O’Brien, Akira Furusawa, Jelena Vuckovic A

arXiv:1003.3928•Nature Photonics•Nov 30, 2009•2,387 citations

Distributed quantum computing across a two-node trapped-ion quantum network

Main, Dougal

Distributed quantum computing (DQC) combines the computing power of multiple networked quantum processing modules, ideally enabling the execution of large quantum circuits without compromising performance or connectivity. Photonic networks are well-suited as a versatile and reconfigurable interconnect layer for DQC; remote entanglement shared between matter qubits across the network enables all-to-all logical connectivity via quantum gate teleportation (QGT). For a scalable DQC architecture, the QGT implementation must be deterministic and repeatable; until now, no demonstration has satisfied these requirements. In this thesis, we report the first experimental demonstration of distributed quantum computing across a quantum network of two photonically interconnected trapped-ion modules. Our experimental apparatus comprises two trapped-ion modules, separated by ∼ 2 m, each co-trapping one 43Ca+ ion and one 88Sr+ ion. The 43Ca+ ions provide a robust quantum memory for the modules, while the 88Sr+ ions provide an interface to the optical quantum network, enabling the remote entanglement of 88Sr+ ions in separate modules. Using local mixed-species operations, we demonstrate the enhanced functionality of the mixed-species trapped-ion quantum network by creating more complex remotely entangled states. These include remote 88Sr+- 43Ca+ and 43Ca+-43Ca+ entanglement, as well as 3- and 4-qubit mixed-species GHZ states. Additionally, we demonstrate the storage of remote entanglement in the 43Ca+ ions for up to 10 s. Utilising these techniques, we demonstrate the distribution of quantum computations across the mixed-species trapped-ion quantum network. Using heralded remote entanglement between the 88Sr+ ions, we deterministically teleport controlled Z gates between two 43Ca+ ions in separate modules, achieving a fidelity of 86 %. We then implement distributed iSWAP and SWAP circuits, compiled with 2 and 3 instances of QGT, respectively, demonstrating the ability to distribute arbitrary two-qubit operations. Finally, we execute Grover’s search algorithm – the first implementation of a distributed quantum algorithm comprising multiple non-local two-qubit gates – and measure a success rate of 71 %. These results represent a significant step towards realising a large-scale distributed quantum computer.

•Jan 1, 2024•0 citations
Distributed quantum computing (DQC) is a promising proposal for overcoming the scalability challenges of quantum computing. However, the evaluation of DQC hardware and software is difficult due to the relative dearth of classical simulation tools available for DQC devices. In this work, we introduce dqc_simulator, a novel simulation toolkit, written in Python, which automates many of the most challenging aspects of the DQC simulation workflow. dqc_simulator enables the easy simulation of both hardware and software, making it easy to create realistic and robust tests and benchmarks for the full DQC stack.
We propose Shuttling-based Distributed Quantum Computing (SDQC), a hybrid architecture that combines the strengths of physical qubit shuttling and distributed quantum computing to enable scalable trapped-ion quantum computing. SDQC performs non-local quantum operations by distributing entangled ion qubits via deterministic shuttling, combining the high-fidelity and deterministic operations of shuttling-based architectures with the parallelism and pipelining advantages of distributed quantum computing. We present (1) a practical architecture incorporating quantum error correction (QEC), (2) pipelining strategies to exploit parallelism in entanglement distribution and measurement, and (3) a performance evaluation in terms of logical error rate and clock speed. For a 256-bit elliptic-curve discrete logarithm problem (ECDLP) instance, which requires 2,871 logical qubits at code distance 13, SDQC achieves a logical error rate which is 1.20−0.45+0.94×10−81.20^{+0.94}_{-0.45}\times10^{-8}1.20−0.45+0.94​×10−8 of Photonic DQC error rate and 3.79−2.84+5.09×10−33.79^{+5.09}_{-2.84}\times10^{-3}3.79−2.84+5.09​×10−3 of Quantum Charge-Coupled Device (QCCD) error rate, while providing 2.82 times faster logical clock speed than QCCD.
Distributed quantum computing (DQC) combines the computing power of multiple networked quantum processing modules, ideally enabling the execution of large quantum circuits without compromising performance or connectivity. Photonic networks are well-suited as a versatile and reconfigurable interconnect layer for DQC; remote entanglement shared between matter qubits across the network enables all-to-all logical connectivity via quantum gate teleportation (QGT). For a scalable DQC architecture, the QGT implementation must be deterministic and repeatable; until now, no demonstration has satisfied these requirements. In this thesis, we report the first experimental demonstration of distributed quantum computing across a quantum network of two photonically interconnected trapped-ion modules. Our experimental apparatus comprises two trapped-ion modules, separated by ∼ 2 m, each co-trapping one 43Ca+ ion and one 88Sr+ ion. The 43Ca+ ions provide a robust quantum memory for the modules, while the 88Sr+ ions provide an interface to the optical quantum network, enabling the remote entanglement of 88Sr+ ions in separate modules. Using local mixed-species operations, we demonstrate the enhanced functionality of the mixed-species trapped-ion quantum network by creating more complex remotely entangled states. These include remote 88Sr+- 43Ca+ and 43Ca+-43Ca+ entanglement, as well as 3- and 4-qubit mixed-species GHZ states. Additionally, we demonstrate the storage of remote entanglement in the 43Ca+ ions for up to 10 s. Utilising these techniques, we demonstrate the distribution of quantum computations across the mixed-species trapped-ion quantum network. Using heralded remote entanglement between the 88Sr+ ions, we deterministically teleport controlled Z gates between two 43Ca+ ions in separate modules, achieving a fidelity of 86 %. We then implement distributed iSWAP and SWAP circuits, compiled with 2 and 3 instances of QGT, respectively, demonstrating the ability to distribute arbitrary two-qubit operations. Finally, we execute Grover’s search algorithm – the first implementation of a distributed quantum algorithm comprising multiple non-local two-qubit gates – and measure a success rate of 71 %. These results represent a significant step towards realising a large-scale distributed quantum computer.