Rail Vision: Quantum Transportation Unveils Transformer Neural Decoder That Outperforms Classical QEC Algorithms in Simulations

Rhea-AI Summary

Rail Vision (Nasdaq: RVSN) announced on February 5, 2026 that its majority-owned subsidiary Quantum Transportation developed a first-generation transformer-based neural decoder for quantum error correction (QEC).

The prototype reportedly outperformed classical decoders like MWPM and Union-Find in comprehensive simulations across multiple codes, noise models, and code distances, and the company says it has a defined intellectual property strategy to protect the approach.

Positive

• Prototype transformer neural decoder reportedly outperforms MWPM and Union-Find in simulations
• Demonstrated generalization across multiple code distances, error rates, and noise profiles
• Completed an intellectual property strategy to secure a defensible QEC position

Negative

• Results are simulation-based; no reported deployment on physical quantum hardware
• Decoder is first-generation prototype with long-term commercial applicability still exploratory

Market Reality Check

Price: $4.19 Vol: Volume 418,219 is about 2...

high vol

$4.19 Last Close

Volume Volume 418,219 is about 2.6x the 20-day average of 162,097, indicating elevated trading interest. high

Technical Price at 4.45 trades well below the 200-day MA of 11.64, after a -23.78% day.

Peers on Argus

RVSN fell -23.78% on high volume while key peers showed mixed moves: SWVL appear...

1 Down

RVSN fell -23.78% on high volume while key peers showed mixed moves: SWVL appeared in momentum scans to the downside, but others like GBX and FSTR were modestly up or down. No broad railroad-sector downside move is evident.

Historical Context

5 past events · Latest: Jan 15 (Positive)

Pattern 5 events

Date	Event	Sentiment	Move	Catalyst
Jan 15	Quantum decoder update	Positive	+3.9%	Prototype transformer-based neural decoder outperformed classical decoders in simulations.
Jan 14	Quantum stake acquisition	Positive	-8.7%	Completion of 51% Quantum Transportation acquisition via share issuance and loan facility.
Jan 06	India market POC	Positive	+8.8%	Proof-of-concept Mainline demo for senior Indian railway stakeholders and officials.
Jan 02	CES tech showcase	Positive	+4.6%	Israel Railways showcased Rail Vision’s AI obstacle-detection and safety systems at CES 2026.
Dec 15	European AI patent	Positive	-6.0%	European patent granted for AI-based railway collision avoidance system and architecture.

Pattern Detected

Recent fundamentally positive headlines have produced mixed reactions, with both rallies and selloffs following IP, market expansion, and quantum-computing updates.

Recent Company History

Over the past few months, Rail Vision reported several milestones: an India proof-of-concept on Jan. 6, 2026, CES 2026 exposure on Jan. 2, 2026, and a European AI collision-avoidance patent on Dec. 15, 2025. It also acquired a 51% stake in Quantum Transportation and announced the initial neural decoder results in mid-January. Today’s update reiterates and deepens that quantum-decoder narrative rather than introducing a new commercial rail contract.

Market Pulse Summary

This announcement details a validated transformer-based neural decoder that outperformed classical q...

Analysis

This announcement details a validated transformer-based neural decoder that outperformed classical quantum error-correction algorithms in simulations, extending the quantum-computing theme first highlighted in mid-January 2026. It underscores Rail Vision’s strategy of pairing Quantum Transportation’s quantum-AI IP with its AI railway-safety stack. Investors may want to track future disclosures that clarify commercialization paths, integration into core rail products, and any follow-on contracts or field deployments tied to these computing advances.

Key Terms

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

6 terms

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.

quantum error correction technical

"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.

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.

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.

intellectual property technical

"Completion of a solid intellectual property strategy, securing a defensible position"

Intellectual property are legal rights that protect creations of the mind—such as inventions, brand names, designs, software, or secret formulas—giving the owner control over who can use, copy or sell them. For investors, IP is like owning a blueprint or recipe: it can generate steady income through exclusive sales or licensing, boost a company’s competitive edge and valuation, and also create costs or risks if rights must be defended or challenged in court.

AI-generated analysis. Not financial advice.

Simulation results show enhanced logical error suppression and real-time decoding potential

Ra’anana, Israel, Feb. 05, 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, recently announced 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.

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 and 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. 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 (RVSN) announce about Quantum Transportation on February 5, 2026?

They announced a first-generation transformer-based neural decoder that outperformed classical QEC algorithms in simulations. According to Rail Vision, the prototype showed superior decoding accuracy and efficiency versus MWPM and Union-Find across varied codes and noise models.

How does the transformer neural decoder compare to classical algorithms like MWPM for RVSN?

The transformer decoder reportedly outperformed MWPM and Union-Find in simulation benchmarks. According to Rail Vision, comparative analysis showed better decoding accuracy and efficiency across multiple quantum error correction codes and realistic noise environments.

Is Rail Vision (RVSN) deploying the neural decoder on real quantum hardware now?

No — the announcement describes simulation results and a prototype, not hardware deployment. According to Rail Vision, the decoder currently targets quantum computing research applications while hardware integration remains future work.

What intellectual property steps did Quantum Transportation take, according to RVSN?

They completed a solid intellectual property strategy to protect the neural QEC paradigm. According to Rail Vision, this secures a defensible position for the transformer-based, code-agnostic decoding approach.

Could Rail Vision's (RVSN) neural decoder affect Rail Vision's railway-safety business?

Potential applicability is exploratory and long-term, not immediate. According to Rail Vision, companies are exploring how similar data analysis and computing methods could be applied to Rail Vision’s core vision and railway-safety technologies.