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Trapped Ion Quantum Computing
Realization of a Quantum Streaming Algorithm on Long-lived Trapped-ion Qubits
arXiv
Authors: Pradeep Niroula, Shouvanik Chakrabarti, Steven Kordonowy, Niraj Kumar, Sivaprasad Omanakuttan, Michael A. Perlin, M. S. Allman, J. P. Campora, Alex Chernoguzov, Samuel F. Cooper, Robert D. Delaney, Joan M. Dreiling, Brian Estey, Caroline Figgatt, Cameron Foltz, John P. Gaebler, Alex Hall, Ali A. Husain, Akhil Isanaka, Colin J. Kennedy, Nikhil Kotibhaskar, Ivaylo S. Madjarov, Michael Mills, Alistair R. Milne, Louis Narmour, Annie J. Park, Adam P. Reed, Kartik Singhal, Anthony Ransford, Justin G. Bohnet, Brian Neyenhuis, Rob Otter, Ruslan Shaydulin
Year
2025
Paper ID
17589
Status
Preprint
Abstract Read
~2 min
Abstract Words
142
Citations
N/A
Abstract
Large classical datasets are often processed in the streaming model, with data arriving one item at a time. In this model, quantum algorithms have been shown to offer an unconditional exponential advantage in space. However, experimentally implementing such streaming algorithms requires qubits that remain coherent while interacting with an external data stream. In this work, we realize such a data-streaming model using Quantinuum Helios trapped-ion quantum computer with long-lived qubits that communicate with an external server. We implement a quantum pair sketch, which is the primitive underlying many quantum streaming algorithms, and use it to solve Hidden Matching, a problem known to exhibit a theoretical exponential quantum advantage in space. Furthermore, we compile the quantum streaming algorithm to fault-tolerant quantum architectures based on surface and bivariate bicycle codes and show that the quantum space advantage persists even with the overheads of fault-tolerance.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- Large classical datasets are often processed in the streaming model, with data arriving one item at a time.
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