Speaker
Description
Scaling quantum devices while preserving coherence remains a central challenge for practical quantum computation. Quantum error correction (QEC) addresses this, but its implementation depends critically on hardware constraints. By the threshold theorem, errors can be suppressed arbitrarily by increasing the number of physical qubits if physical error rates fall below a code-dependent threshold, motivating architectures that combine scalability with high-fidelity qubits.
Trapped ions are a promising platform for QEC due to their high fidelities and connectivity, both advantageous for codes encoding a high number of logical qubits per physical qubits [1, 2]. Decoding speeds are also unlikely to be limiting due to the relatively slow gate speeds. The challenge is then to scale the architecture without degrading its appealing properties.
Traditional approaches scale trapped-ion systems either by extending ion chains or by shuttling ions between zones. Both define a single quantum processing unit (QPU) and face limitations at large scales: long chains suffer from overly crowded motional mode frequencies, while shuttling introduces overhead from transport and cooling. More broadly, scaling a single QPU appears challenging across qubit platforms.
Modular architectures offer a complementary approach, where multiple moderate-sized QPUs connected via entanglement enable universal quantum computation. This shifts the challenge from scaling individual QPUs to reliably generating inter-module entanglement, which has been demonstrated in trapped ions using photonic links [3, 4].
In this work, we study a “maximally modular” architecture where each QPU contains a minimal number of (around 2–5) ions [5, 6]. This keeps local operations fast and reliable, but makes probabilistic, noisy entanglement generation the main bottleneck. Its stochastic nature complicates QEC scheduling, as asynchronously generated entanglement must be coordinated to minimise redundancy and errors.
We develop scheduling strategies across multiple layers, including parallelised parity checks, cutoff policies for aborting delayed operations, and synchronised entanglement attempts. We numerically simulate the performance of our implementation using a realistic error model based on modular trapped-ion devices. Our analysis provides a practical framework for implementing distributed QEC in trapped-ions and beyond.
[1] N. P. Breuckmann et al., Quantum low-density parity-check codes, PRX Quantum 2, 040101 (2021).
[2] S. Bravyi et al., High-threshold and low-overhead fault-tolerant quantum memory, Nature 627, 778 (2024).
[3] L. J. Stephenson et al., High-rate, high-fidelity entanglement of qubits across an elementary quantum network, Phys. Rev. Lett. 124, 110501 (2020).
[4] D. Main et al., Distributed quantum computing across an optical network link, Nature 638, 383 (2025).
[5] N. H. Nickerson et al., Topological quantum computing with a very noisy network and local error rates approaching one percent, Nature Communications 4, 1756 (2013).
[6] N. H. Nickerson et al., Freely scalable quantum technologies using cells of 5-to-50 qubits with very lossy and noisy photonic links, Phys. Rev. X 4, 041041 (2014).
| Academic level | PhD student |
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