Rail Vision: Quantum Transportation Successfully Integrates Google’s Public Surface-Code Dataset into its Quantum Error Correction Transformer
Ra’anana,
In this phase, the team implemented a standardized data adapter to ingest dense binary syndrome measurements from selected experimental configurations, engineered dynamic attention masking that adapts to code distances and layouts, and established an end-to-end training loop capable of processing mixed batches of real experimental shots.
This milestone reduces technical risk by advancing QECC beyond controlled internal data formats and lays the foundation required for scalable training and repeatable benchmarking on a credible external testbed.
Quantum Transportation is developing transformer-based quantum decoder technology for advanced quantum error correction, including cloud-deployed neural decoders. The decoder’s IP (patent pending) is licensed from Ramot at
Quantum Transportation previously announced it has successfully implemented its transformer-based neural decoder on the AWS cloud, marking a significant milestone toward real-world quantum applications within the transportation sector. Building on the recent unveiling of its transformer neural decoder, which outperformed classical quantum error correction (QEC) algorithms in simulations, and the delivery of its first prototype for universal error correction, Quantum Transportation's cloud deployment now provides the scalable infrastructure needed to process complex quantum data efficiently.
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The Company’s cloud-based platform complements its products by transforming railway operational data into actionable insights that help optimize performance, reduce downtime, and improve safety. As the Company expands its global footprint, it delivers AI-driven perception that supports safer operations, reduces operational risk, and enables the transition to fully autonomous operations.
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Chief Executive Officer
15 Ha'Tidhar St
Ra'anana, 4366517 Israel
Telephone: +972- 9-957-7706
Investor Relations:
investors@railvision.io
Source: Rail Vision Ltd.
