Quantum X Labs 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
Gix Internet Ltd. (Nasdaq: GIXI; TASE: GIX) (“Gix” or the “Company”), a dual-listed technology company, today announced that Quantum X Labs Inc. (Nasdaq: QXL) (“Quantum X Labs” or “QXL”), in which Gix holds approximately 13% (2,818,585 shares), has achieved 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 QXL’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.
Quantum X Labs 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, Quantum X Labs 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, Quantum X Labs 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, Quantum X Labs 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.
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