Rail Vision: Quantum Transportation Delivers First Transformer-Based Neural Decoder for Universal Quantum Error Correction

Rhea-AI Summary

Rail Vision (NASDAQ: RVSN) said its majority-owned subsidiary Quantum Transportation developed a prototype transformer-based neural decoder for universal quantum error correction. The code-agnostic decoder reportedly outperformed classical decoders (MWPM, Union-Find) in simulations across multiple codes, noise profiles and code distances, and the company says it has a completed IP strategy to protect the approach. The work targets quantum computing research applications with potential long-term relevance to Rail Vision's data analysis roadmap.

Market Reality Check

Price: $0.3357 Vol: Volume 45,745,744 is 14.5...

high vol

$0.3357 Last Close

Volume Volume 45,745,744 is 14.57x the 20-day average of 3,138,661, indicating unusually heavy trading ahead of and around this announcement. high

Technical Shares at 0.3232 are trading below the 200-day MA of 0.39, and about 79.93% under the 52-week high of 1.61 while sitting 18.22% above the 52-week low of 0.2734.

Peers on Argus

RVSN fell 8.7% with very elevated volume, while key Industrials/Railroads peers ...

RVSN fell 8.7% with very elevated volume, while key Industrials/Railroads peers listed mostly showed relatively modest declines (from -0.03% to -1.96%), except CVV at -9.81%. No peers appeared on the momentum scanner, reinforcing this as a stock-specific reaction rather than a broad sector move.

Historical Context

5 past events · Latest: Jan 14 (Positive)

Pattern 5 events

Date	Event	Sentiment	Move	Catalyst
Jan 14	Quantum acquisition close	Positive	-8.7%	Completed 51% Quantum Transportation acquisition with share issuance and loan facility.
Jan 06	India POC launch	Positive	+8.8%	Launched Indian Mainline POC for senior officials and rail stakeholders.
Jan 02	CES 2026 showcase	Positive	+4.6%	Israel Railways to showcase Rail Vision safety tech at CES 2026.
Dec 15	European AI patent	Positive	-6.0%	European Patent Office granted AI-based collision avoidance patent.
Dec 12	Quantum deal signing	Positive	-0.9%	Signed agreement to acquire 51% of Quantum Transportation with convertible loan.

Pattern Detected

Recent history shows several instances where seemingly positive strategic or IP-related news for Rail Vision coincided with negative short-term price reactions.

Recent Company History

This announcement follows Rail Vision’s recent acquisition of a 51% stake in Quantum Transportation and prior agreements to develop quantum-AI rail safety applications. Over the past months, the company highlighted Indian market expansion, visibility at CES 2026, and a key European patent for AI-based collision avoidance, alongside earlier Quantum Transportation deal terms. Price reactions to these largely positive milestones have been mixed, with some events (like India POC and CES showcase) seeing gains, while patent and quantum-deal updates saw declines, indicating inconsistent trading responses to strategic news.

Market Pulse Summary

This announcement highlights Quantum Transportation’s transformer-based neural decoder for scalable ...

Analysis

This announcement highlights Quantum Transportation’s transformer-based neural decoder for scalable quantum error correction, validated against leading classical decoders across multiple codes and noise profiles. It builds on Rail Vision’s recent acquisition of a majority stake in Quantum Transportation and its broader AI and patent activity in rail safety. Investors may track how, over time, this quantum-AI work intersects with Rail Vision’s core obstacle-detection offerings and whether follow-on updates shift the company’s commercialization roadmap or partnership opportunities.

Key Terms

quantum error correction, transformer-based neural decoder, minimum-weight perfect matching, union-find, +2 more

6 terms

quantum error correction technical

"solution designed to advance scalable quantum error correction (QEC)."

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.

transformer-based neural decoder technical

"successful prototype development and rigorous validation of its first-generation transformer-based neural decoder"

A transformer-based neural decoder is the part of an advanced AI model that generates output—like text, code, or predictions—by turning learned patterns into a coherent response. Think of it as the model’s writer or composer that arranges pieces of information into a final answer; it matters to investors because its quality drives AI product performance, user experience, potential revenue, compute costs, and risks such as errors or biased outputs that can affect a company’s value.

minimum-weight perfect matching technical

"compared to leading classical algorithms, such as Minimum-Weight Perfect Matching (MWPM) and Union-Find."

A minimum-weight perfect matching is a mathematical method for pairing up every item in a set so that every item has exactly one partner and the total cost of all pairs is as small as possible; here “weight” means a cost, distance, or penalty assigned to each possible pair. For investors, it matters because the same idea underlies algorithms used to optimize trades, match orders, allocate assets or pair risks—finding the cheapest complete set of connections can reduce transaction costs and improve portfolio efficiency, much like pairing shoes to minimize mismatches.

union-find technical

"compared to leading classical algorithms, such as Minimum-Weight Perfect Matching (MWPM) and Union-Find."

A union-find is a simple software tool that keeps track of which items belong to the same group, letting a program quickly merge groups or check whether two items are connected. Think of it like an efficient guest list manager who can rapidly tell you whether two people are at the same table and can combine tables with minimal fuss; for investors, it matters because this kind of low-level efficiency can reduce computing costs, improve product speed and reliability, and scale systems as users or data grow.

surface code technical

"across diverse quantum error correction codes (including surface code variants) and realistic noise"

A surface code is a method used in quantum computing to detect and correct errors by arranging qubits in a grid where patterns of measurements reveal faults, much like a quilt pattern helping you spot and mend a torn patch. It matters to investors because robust error correction is a key hurdle to building practical, scalable quantum computers; progress or setbacks in surface-code implementations can materially affect a company’s technology roadmap, costs, timelines and competitive position.

machine-learning technical

"a highly generalizable, machine-learning-driven approach capable of outperforming conventional"

Machine-learning is a type of computer software that improves its performance by finding patterns in data rather than following fixed rules. Think of it like a digital apprentice that gets better at tasks — such as spotting customer trends, predicting demand, or automating routine work — the more examples it sees. Investors care because machine-learning can boost revenue, cut costs, create competitive advantages, and introduce new risks tied to data quality and model errors.

AI-generated analysis. Not financial advice.

Achieves superior decoding accuracy and dramatically improved efficiency compared to leading classical algorithms

Ra’anana, Israel, Jan. 15, 2026 (GLOBE NEWSWIRE) -- Rail Vision Ltd. (Nasdaq: RVSN) (“Rail Vision” or the “Company”), an early commercialization stage technology company seeking to revolutionize railway safety and the data-related market, announced today that its majority owned subsidiary Quantum Transportation Ltd. (“Quantum Transportation”), a quantum computing innovator, has achieved a major technical breakthrough with the successful prototype development and rigorous validation of its first-generation transformer-based neural decoder - a pioneering, code-agnostic solution designed to advance scalable quantum error correction (QEC).

This innovative decoder harnesses advanced transformer architectures to provide a highly generalizable, machine-learning-driven approach capable of outperforming conventional decoding methods. In comprehensive simulations across diverse quantum error correction codes (including surface code variants) and realistic noise environments, the system has demonstrated superior decoding accuracy and efficiency compared to leading classical algorithms, such as Minimum-Weight Perfect Matching (MWPM) and Union-Find.

Highlights of this achievement include:

• Design and finalization of a proprietary transformer architecture specifically optimized for the complex, high-dimensional structure of quantum error syndromes
• In-depth benchmarking and comparative analysis against the current state-of-the-art in QEC decoding techniques
• Strong evidence of generalization across multiple code distances, error rates, and varying noise profiles
• Completion of a solid intellectual property strategy, securing a defensible position for this transformative neural QEC paradigm

This breakthrough aims to support the ongoing collaboration between Rail Vision and Quantum Transportation by combining Quantum Transportation’s quantum-AI based intellectual property and innovation with Rail Vision’s advanced vision and railway-safety technologies. While the decoder is currently focused on quantum computing research applications, the companies are exploring, on a long-term basis, potential areas where similar data analysis and computing methodologies could be applicable to Rail Vision’s core technology.

David BenDavid, CEO of Rail Vision said: “We are pleased with the continued progress at Quantum Transportation. We believe that this breakthrough reflects the strength of its research capabilities and reinforces the strategic optionality of our investment as we evaluate future technology pathways.”

About Quantum Transportation

Quantum Transportation proposes to develop a Quantum Error Correction Simulator powered by a patented Transformer-based Universal Decoder (PD). This decoder, leveraging deep learning techniques, generalizes across quantum codes, learns from noise patterns, and delivers a scalable and hardware-agnostic approach to error correction. The patented Deep Quantum Error Correction Transformer (DQECCT) introduces a novel machine-learning decoder that predicts and refines quantum errors using transformer-based architectures, incorporates masking layers derived from parity-check matrices and optimizes a combined loss function over Logical Error Rate (LER), Bit Error Rate (BER), and Noise Estimation Error. This technology aspires to outperform classical decoders (e.g., MWPM) in both accuracy and speed and uniquely handles faulty measurement scenarios. It is adaptable to various codes - including Surface, Color, Bicycle, and Product Codes.

About Rail Vision Ltd.

Rail Vision is a development stage technology company that is seeking to revolutionize railway safety and the data-related market. The company has developed cutting edge, artificial intelligence based, industry-leading technology specifically designed for railways. The company has developed its railway detection and systems to save lives, increase efficiency, and dramatically reduce expenses for the railway operators. Rail Vision believes that its technology will significantly increase railway safety around the world, while creating significant benefits and adding value to everyone who relies on the train ecosystem: from passengers using trains for transportation to companies that use railways to deliver goods and services. In addition, the company believes that its technology has the potential to advance the revolutionary concept of autonomous trains into a practical reality. 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. Words such as “expects,” “anticipates,” “intends,” “plans,” “believes,” “seeks,” “estimates” and similar expressions or variations of such words are intended to identify forward-looking statements. Such expectations, beliefs and projections are expressed in good faith. For example, Rail Vision is using forward-looking statements when it discusses the ongoing collaboration between Rail Vision and Quantum Transportation by combining Quantum Transportation’s quantum-AI based intellectual property and innovation with Rail Vision’s advanced vision and railway-safety technologies, how the companies are exploring, on a long-term and non-committal basis, potential areas where similar data analysis and computing methodologies could be applicable to Rail Vision’s core technology, the continued progress at Quantum Transportation and its belief that this breakthrough reflects the strength of Quantum Transportation’s research capabilities and reinforces the strategic optionality of Rail Vision’s investment as it evaluates future technology pathways. 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 filed with the SEC on March 31, 2025. 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

What did Rail Vision announce on January 15, 2026 about Quantum Transportation (RVSN)?

Rail Vision announced that Quantum Transportation developed a prototype transformer-based neural decoder for universal quantum error correction and validated it in simulations.

How did the transformer-based decoder perform versus classical algorithms in RVSN's announcement?

The company reported the decoder demonstrated superior decoding accuracy and improved efficiency compared with leading classical algorithms such as MWPM and Union-Find in simulations.

Is the new quantum decoder from Quantum Transportation immediately commercial for Rail Vision (RVSN)?

No; the decoder is focused on quantum computing research applications today, with potential long-term applicability to Rail Vision's core technologies being explored.

Does Rail Vision (RVSN) claim intellectual property protection for the neural decoder?

Yes; the announcement states Quantum Transportation completed an intellectual property strategy intended to secure a defensible position for the neural QEC approach.

What technical scope did Rail Vision mention for the transformer decoder (RVSN)?

The decoder is described as code-agnostic, benchmarked across diverse QEC codes including surface-code variants, multiple code distances, error rates, and realistic noise profiles.

How does Rail Vision describe the strategic value of the Quantum Transportation breakthrough for shareholders (RVSN)?

Rail Vision said the breakthrough reinforces the strategic optionality of its investment by combining Quantum Transportation's quantum-AI IP with Rail Vision's vision and railway-safety technologies.