Quantum X Labs Expands AI-Powered Quantum Error Correction Program to Three IBM Quantum Processors, Demonstrating Up to 20% Fewer Logical Decoding Failures

Preliminary IBM dataset tests extend the company's validation work beyond its earlier research using Google hardware datasets.

Rhea-AI Summary

Quantum X Labs (QXL) expanded its AI-powered quantum error correction benchmarking program to datasets from three IBM quantum processors.

Preliminary tests demonstrated up to 20% fewer logical decoding failures, using improved noise-model calibration to help identify errors in quantum computations. The program covers IBM Fez, IBM Kingston and IBM Pittsburgh and integrates experimental measurements, calibration, GPU-accelerated AI training and decoder evaluation. Building on earlier research using Google hardware datasets, the company aims to develop error correction technology that can adapt to different quantum computing architectures.

Positive

• Minor pointImproved noise-model calibration demonstrated up to 20% fewer logical decoding failures in preliminary IBM dataset tests.
• Minor pointBenchmarking expanded to three IBM processors: IBM Fez, IBM Kingston and IBM Pittsburgh.

Negative

• Minor pointDecoding improvements remain preliminary benchmark results using experimental processor datasets.

Key Figures

Fewer logical decoding failures: Up to 20% IBM quantum processors: 3 processors

Fewer logical decoding failures

Up to 20%

Preliminary benchmarks using experimental data from IBM quantum processors

IBM quantum processors

3 processors

Expanded experimental benchmarking program

Previous AI Reports

2 past events · Latest: Oct 21

Same Type 2 events

1. Oct 21

   QEC benchmark

   24h Move

   +4.6%

   Reported fewer logical errors on Google's public hardware data using its decoder.

2. Aug 21

   QEC results

   24h Move

   +8.8%

   Reported improved AI-assisted decoder performance against matching-family benchmarks on Google's surface-code dataset.

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

Key Terms

quantum error correction, noise-model calibration, surface-code datasets, fault-tolerant quantum computing

4 terms

quantum error correction technical

"milestone in its artificial intelligence (AI)-powered quantum error correction technology"

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.

noise-model calibration technical

"achieved through the Company's advanced noise-model calibration methodology"

The process of adjusting a statistical model that represents random errors or 'noise' so its parameters and assumptions match the actual noise seen in data. That typically means estimating quantities like variance, autocorrelation, or distribution shape from observed residuals and updating the model (by methods such as maximum likelihood, method of moments, or residual analysis) so forecasts, filters, or hypothesis tests account for real-world randomness. Calibration makes the noise model produce realistic uncertainty estimates, but it can be sensitive to the sample used and can become misleading if noise properties change over time or if the model is overfitted to idiosyncratic data.

surface-code datasets technical

"earlier research using experimental surface-code datasets"

Datasets produced by or for the surface code, a leading quantum error‑correcting scheme. They typically record rounds of qubit stabilizer measurements (syndromes), timing and gate operations, and any known physical error events from experiments or simulations; researchers use them to test and train decoders, evaluate logical error rates, and study how real hardware noise maps into logical failures. The term applies specifically to data tied to surface‑code layouts and protocols, not to general quantum or classical datasets, so entries in such a dataset encode the measurement outcomes and context needed to reconstruct or analyze error chains under the surface‑code rules.

fault-tolerant quantum computing technical

"toward the broader industry objective of fault-tolerant quantum computing"

Fault-tolerant quantum computing is the ability of a quantum computer to keep producing correct results even when its basic parts make mistakes, by detecting and fixing errors and using redundancy so the machine continues to work reliably. For investors, it matters because fault tolerance is the key to scaling quantum machines from experimental demos into practical, revenue-generating systems—think of it like having backups and automatic repairs that make a prototype road-ready and lower the technology’s commercial and technical risk.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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The Company has expanded its AI-powered quantum error correction program across three IBM quantum processors, building on its earlier work using datasets generated by Google quantum hardware and advancing its hardware-agnostic decoding technology.

TEL AVIV, Israel, Nov. 09, 2026 (GLOBE NEWSWIRE) -- Quantum X Labs Inc. (NASDAQ: QXL) (“Quantum X Labs” or the “Company”), a quantum technology company developing quantum computing, quantum sensing, quantum error correction and quantum applications technologies, today announced a significant technological milestone in its artificial intelligence (AI)-powered quantum error correction technology, demonstrating up to 20% fewer logical decoding failures in preliminary benchmarks using experimental data from IBM quantum processors.

The results were achieved through the Company's advanced noise-model calibration methodology, designed to improve the ability of quantum error correction decoders to identify and correct errors arising during quantum computations. The preliminary benchmarks demonstrated improvements in decoding performance, representing another step toward developing more reliable and scalable quantum computing systems.

The Company has expanded its experimental benchmarking program to datasets generated by three IBM quantum processors — IBM Fez, IBM Kingston and IBM Pittsburgh — establishing a comprehensive end-to-end workflow that integrates experimental quantum measurements, noise-model calibration, GPU-accelerated AI training and decoder performance evaluation.

Advancing Quantum Computing Reliability Through AI- Quantum error correction is widely recognized as one of the most critical technological challenges facing the quantum computing industry. Quantum processors are inherently sensitive to environmental interference, hardware imperfections and operational noise, which can introduce errors into quantum computations and limit their practical reliability.

Quantum X Labs is developing an AI-powered quantum error correction architecture designed to address these challenges by combining hardware-specific noise characterization with advanced machine-learning techniques and GPU-accelerated decoder training.

Through its latest benchmarking program, the Company demonstrated that improved noise-model calibration can enhance logical decoding performance, achieving up to 20% fewer logical decoding failures in preliminary tests on IBM processor datasets.

The latest milestone builds on Quantum X Labs' earlier research using experimental surface-code datasets associated with Google's quantum computing technology.

By expanding its testing to IBM quantum processors, the Company is broadening its validation activities across multiple hardware vendors and quantum error correction code families.

This multi-platform approach is an important component of Quantum X Labs' long-term strategy to develop hardware-agnostic quantum error correction solutions capable of adapting to different quantum computing architectures, noise characteristics and operational environments.

“We believe that the ability to improve logical decoding performance on different quantum processors represents an important step toward the broader industry objective of fault-tolerant quantum computing,” said Prof. Nir Sharon, the Company’s Chief Quantum Scientist, “As quantum processors continue to increase in size and complexity, efficient quantum error correction is expected to become an essential component of practical quantum computing systems.”

These activities form part of Quantum X Labs' broader strategy to develop advanced quantum computing infrastructure and software technologies designed to support the transition from today's experimental quantum processors toward more reliable, commercially viable quantum computing platforms.

About Quantum X Labs Inc.

Quantum X Labs Inc. and its subsidiaries are focused on quantum technology, digital advertising and computing and enterprise artificial intelligence (AI) solutions. Quantum X Labs Ltd. is focused on developing and promoting quantum algorithms for the transportation, drug discovery and security segments as well as developing quantum- based GPS replacement and quantum atom accuracy solutions. Gix Media develops a variety of technological software solutions, which perform automation, optimization and monetization of internet campaigns, for the purposes of acquiring and routing internet user traffic to its customers. Metagramm is a developer of grammatical error correction software and offers tools for writing and reviewing, grammar, spelling, punctuation and style features, as well as translation and multilingual dictionaries, using artificial intelligence and machine learning technology.

For more information about Quantum X Labs, visit https://quantumxlabs.xyz/

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the “safe harbor” provisions of the Private Securities Litigation Reform Act of 1995 and other Federal securities laws. Forward-looking statements contained in this press release include, but are not limited to, statements regarding Quantum X Labs’ and its subsidiaries’ 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. For example, the Company is using forward-looking statements when it discusses the potential significance and benefits of its AI-powered quantum error correction technology; its efforts to improve logical decoding performance through noise-model calibration, GPU-accelerated AI training and related methods; the potential for its technology to support more reliable and scalable quantum computing systems; its development of hardware-agnostic quantum error correction solutions across different quantum processors, hardware vendors, code families, architectures, noise characteristics and operational environments; and its broader strategy to develop quantum computing infrastructure and software technologies for more reliable and commercially viable quantum computing platforms. 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 most recent Annual Report on Form 10-K 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. Quantum X Labs is not responsible for the content of third-party websites.

IBM, IBM Fez, IBM Kingston and IBM Pittsburgh are trademarks or registered trademarks of International Business Machines Corporation. Use of these names does not imply any affiliation with, or endorsement by, International Business Machines Corporation.

Investor Relations Contacts:

Michal Efraty
Investor Relations
michal@efraty.com 

FAQ

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

What did Quantum X Labs demonstrate in its IBM quantum error correction benchmarks?

Quantum X Labs demonstrated up to 20% fewer logical decoding failures in preliminary tests using experimental IBM processor datasets. The results used improved noise-model calibration, which helps error correction software identify errors arising during quantum computations.