Rail Vision: Quantum Transportation Successfully Integrates Google’s Public Surface-Code Dataset into its Quantum Error Correction Transformer

Rail Vision (Nasdaq:RVSN) announced that majority-owned subsidiary Quantum Transportation integrated Google Quantum AI’s public experimental surface-code dataset into its Quantum Error Correction (QECC) transformer pipeline.

Rhea-AI Summary

Rail Vision (Nasdaq:RVSN) announced that majority-owned subsidiary Quantum Transportation integrated Google Quantum AI’s public experimental surface-code dataset into its Quantum Error Correction (QECC) transformer pipeline.

The team built a standardized data adapter, dynamic attention masking, and an end-to-end training loop for mixed real experimental shots, reducing technical risk and enabling scalable training and benchmarking on an external testbed.

Quantum Transportation’s transformer-based neural decoder IP, licensed from Ramot at Tel Aviv University, is cloud-deployed on AWS and has outperformed classical QEC algorithms in simulations.

Positive

• Integration of Google Quantum AI surface-code dataset into QECC transformer pipeline
• Standardized adapter for dense binary syndrome measurements across experimental configurations
• Dynamic attention masking tuned to code distances and layouts
• End-to-end training loop handling mixed batches of real experimental shots
• Cloud deployment of transformer-based neural decoder on AWS infrastructure
• Decoder simulations that outperformed classical quantum error correction algorithms

Negative

• None.

Argus May 20 session

-18.12% close to close Open Argus

Details

News Market Reaction – RVSN

On May 20, the day this news came out, RVSN closed 18.12% below the previous close.

Data tracked by StockTitan Argus for the May 20 session.

Historical Context

5 past events · Latest: Mar 31

5 events

1. Mar 31

   Full-year 2025 earnings

   24h Move

   +3.7%

   Revenue growth, narrowed net loss, strong cash and 51% Quantum Transportation stake.

2. Mar 24

   Frankfurt listing

   24h Move

   -5.5%

   Dual-listing on FSE aimed at improving liquidity and European investor access.

3. Mar 16

   India MainLine trial

   24h Move

   +3.6%

   Successful two-month MainLine proof of concept with strong operational feedback.

4. Feb 24

   Quantum AWS deployment

   24h Move

   +18.5%

   Quantum Transportation neural decoder deployed on AWS enabling scalable quantum data processing.

5. Feb 23

   Quantum M&A sector

   24h Move

   -0.3%

   Viewbix deal for Quantum X Labs, highlighting broader quantum error-correction interest.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Key Terms

surface-code, quantum error correction, attention masking, neural decoders, +2 more

6 terms

surface-code technical

"integration layer that brings a publicly accessible experimental surface-code dataset from Google Quantum AI"

A surface code is a leading method for protecting quantum bits (the basic units of quantum computers) from errors by arranging them in a two‑dimensional grid and using nearby bits to detect and correct mistakes. Think of it as a built‑in safety net or redundant sensor array that lets a fragile machine keep working reliably. For investors, progress with surface‑code implementations signals how close a company is to building scalable, fault‑tolerant quantum computers—affecting technology timelines, capital needs, and competitive value.

quantum error correction technical

"into the Quantum Transportation’s Quantum Error Correction (QECC) IP (patent pending) transformer pipeline"

Quantum error correction is a set of methods for detecting and fixing mistakes in quantum computers by encoding fragile quantum information across multiple physical parts, much like using multiple copies or checksums to protect a sensitive digital file. For investors, it matters because reliable error correction is a key technical milestone that determines whether quantum machines can scale from experimental devices to practical tools that could disrupt computing, encryption, drug discovery and other industries.

attention masking technical

"engineered dynamic attention masking that adapts to code distances and layouts"

A machine-learning technique that hides or blocks parts of an input so a model only pays attention to permitted information — like putting post‑it notes over words in a sentence so only certain words are visible. For investors, it matters because attention masking can improve the reliability of AI-driven analysis, reduce the chance of sensitive data leakage in automated systems, and affect product quality, regulatory compliance and competitive risk.

neural decoders technical

"including cloud-deployed neural decoders"

Neural decoders are software systems that translate patterns of brain activity or machine-generated signals into readable commands, text, images, or predictions. Think of them as translators that convert raw electrical or AI-generated noise into meaningful output, similar to how a speech-to-text app turns sound into words. For investors, neural decoders matter because they underpin products and services in healthcare, brain-computer interfaces, and advanced AI applications that can create new markets, revenue streams, and regulatory considerations.

AWS cloud technical

"implemented its transformer-based neural decoder on the AWS cloud"

AWS Cloud is Amazon’s collection of internet-based computing services that lets businesses rent computing power, storage and software tools instead of buying and maintaining their own servers. Like renting flexible office space rather than owning a building, using AWS can help companies scale quickly, cut upfront costs and shift spending into operational pay-as-you-go charges; outages, price changes or heavy dependence on a single cloud provider can meaningfully affect a company’s costs, growth and risk profile for investors.

quantum error correction (QEC) technical

"outperformed classical quantum error correction (QEC) algorithms in simulations"

A set of techniques that detect and fix errors in quantum computers so their fragile quantum bits (qubits) can hold accurate information over time. Think of it like a car’s stability control or a spellchecker for a new language: it compensates for inevitable noise and mistakes so the machine gives reliable results. For investors, strong quantum error correction is a key indicator of whether a quantum technology can scale, reduce technical risk, and become commercially valuable.

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Ra’anana, Israel, May 20, 2026 (GLOBE NEWSWIRE) -- Rail Vision Ltd. (Nasdaq: RVSN, FSE: C80) (“Rail Vision” or the “Company”), an early commercialization stage technology company transforming railway safety through advanced AI-integrated sensing systems, announces today, that its majority owned subsidiary Quantum Transportation Ltd. (“Quantum Transportation”), a quantum computing innovator, has successfully delivered a working integration layer that brings a publicly accessible experimental surface-code dataset from Google Quantum AI, into the Quantum Transportation’s Quantum Error Correction (QECC) IP (patent pending) transformer pipeline.

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 Tel Aviv University, with applications in various potential industries and end users.

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.

About Rail Vision Ltd.

Rail Vision (Nasdaq: RVSN) is an early commercialization stage technology company transforming railway safety through advanced AI-integrated sensing systems. The Company develops and commercializes proprietary, multi-spectral electro-optic platforms that provide extended-range situational awareness and real-time hazard detection. Using machine learning algorithms to identify and classify obstacles, Rail Vision’s technology enhances safety, improves operational efficiency, and supports continuity across deployments.

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.

Rail Vision holds a 51% stake in Quantum Transportation, which has an exclusive sub-license for rail technologies under an innovative pending patent in quantum error correction owned by Ramot, the technology transfer company of Tel Aviv University.

For more information, please visit https://www.railvision.io/

Forward-Looking Statements

This press release contains “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act and other securities laws. Forward-looking statements contained in this press release include, but are not limited to, statements regarding Rail Vision’s and its subsidiary’s strategic and business plans, technology, relationships, objectives and expectations for its business, growth, the impact of trends on and interest in its business, intellectual property, products and its future results, operations and financial performance and condition and may be identified by the use of words such as “may,” “seek,” “will,” “consider,” “likely,” “assume,” “estimate,” “expect,” “anticipate,” “intend,” “believe,” “do not believe,” “aim,” “predict,” “plan,” “project,” “continue,” “potential,” “guidance,” “objective,” “outlook,” “trends,” “future,” “could,” “would,” “should,” “target,” “on track” or their negatives or variations, and similar terminology and words of similar import, generally involve future or forward-looking statements. Forward-looking statements are not historical facts, and are based upon management’s current expectations, beliefs and projections, many of which, by their nature, are inherently uncertain. Such expectations, beliefs and projections are expressed in good faith. However, there can be no assurance that management’s expectations, beliefs and projections will be achieved, and actual results may differ materially from what is expressed in or indicated by the forward-looking statements. Forward-looking statements are subject to risks and uncertainties that could cause actual performance or results to differ materially from those expressed in the forward-looking statements. For a more detailed description of the risks and uncertainties affecting the Company, reference is made to the Company’s reports filed from time to time with the Securities and Exchange Commission (“SEC”), including, but not limited to, the risks detailed in the Company’s annual report on Form 20-F, for the fiscal year ended December 31, 2025, filed with the SEC on March 31, 2026 and in subsequent filings with the SEC. Forward-looking statements speak only as of the date the statements are made. The Company assumes no obligation to update forward-looking statements to reflect actual results, subsequent events or circumstances, changes in assumptions or changes in other factors affecting forward-looking information except to the extent required by applicable securities laws. If the Company does update one or more forward-looking statements, no inference should be drawn that the Company will make additional updates with respect thereto or with respect to other forward-looking statements. References and links to websites have been provided as a convenience, and the information contained on such websites is not incorporated by reference into this press release. Rail Vision is not responsible for the contents of third-party websites.

Contacts
David BenDavid
Chief Executive Officer
Rail Vision Ltd.
15 Ha'Tidhar St
Ra'anana, 4366517 Israel
Telephone: +972- 9-957-7706

Investor Relations:
Michal Efraty
investors@railvision.io

FAQ

AI-generated questions and answers. How Rhea-AI works. Not financial advice.

What did Rail Vision (RVSN) announce about Quantum Transportation’s integration with Google’s quantum dataset on May 20, 2026?

Rail Vision announced Quantum Transportation integrated Google Quantum AI’s public surface-code dataset into its QECC transformer pipeline. According to Rail Vision, this includes a standardized data adapter, dynamic attention masking, and an end-to-end training loop for mixed real experimental shots, supporting scalable training and benchmarking.

How does integrating Google’s surface-code dataset benefit Quantum Transportation’s QECC transformer for Rail Vision (RVSN) investors?

The integration enables QECC training on a credible external testbed instead of only internal formats. According to Rail Vision, this reduces technical risk, supports scalable training, and allows repeatable benchmarking of Quantum Transportation’s transformer-based decoders using real experimental quantum error correction data.

What is Quantum Transportation’s transformer-based quantum decoder technology linked to Rail Vision (RVSN)?

Quantum Transportation is developing transformer-based neural decoders for advanced quantum error correction applications. According to Rail Vision, this decoder IP is licensed from Ramot at Tel Aviv University, targets cloud-deployed use, and has shown simulated performance that outperformed classical quantum error correction algorithms.

How is Quantum Transportation using AWS cloud for its quantum error correction technology for Rail Vision (RVSN)?

Quantum Transportation has implemented its transformer-based neural decoder on the AWS cloud. According to Rail Vision, this cloud deployment provides scalable infrastructure to process complex quantum data efficiently and supports progress toward real-world quantum applications, including potential uses in the transportation sector.

What role does Ramot at Tel Aviv University play in Quantum Transportation’s QECC technology for Rail Vision (RVSN)?

Quantum Transportation’s decoder intellectual property is licensed from Ramot at Tel Aviv University. According to Rail Vision, this licensed IP underpins the transformer-based quantum decoder, supporting advanced quantum error correction with potential applications across various industries and end users, including transportation-focused solutions.

How does Quantum Transportation’s transformer neural decoder compare with classical QEC algorithms for Rail Vision (RVSN)?

Quantum Transportation’s transformer neural decoder has outperformed classical QEC algorithms in simulations. According to Rail Vision, this simulated performance, combined with a prototype for universal error correction and cloud deployment, supports ongoing development toward more capable quantum error correction solutions for practical use cases.